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Predicting cycling accident risk in Brussels: A spatial case-control approach
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... For example, Schepers et al. (2011) found that bidirectional bicycle tracks at unsignalised priority intersections are less safe compared to the unidirectional variant. This was also concluded in a literature review by Thomas and De Robertis (2013) and in a study in Brussels by Vandenbulcke, Thomas and Int Panis (2014). All studies explain the increased risk of bidirectional bicycle tracks by the absence of expecting cyclists from opposite direction by drivers and related visual search strategies at intersections. ...
... Discontinuities are here defined as the end or an interruption over some distance of the bicycle Title Safe cycling routes Report R-2022-6A Page 21 facility, often located at intersections. Similar results were found by Vandenbulcke, Thomas and Int Panis (2014), where the discontinuous character of bicycle lanes at intersections leads to an increased crash risk for cyclists. ...
... They should therefore be as limited as possible in a route. As few transitions and discontinuities as possible: Transitions between and discontinuities of cycling infrastructure lead to increased risk of having a crash and result in increased levels of discomfort for the cyclist(Krizek & Roland, 2005;Niaki, Saunier & Miranda-Moreno, 2018;Vandenbulcke, Int Panis & Thomas, 2017;Vandenbulcke, Thomas & Int Panis, 2014). ...
This study deals with cycling safety on a route level. Firstly, it aims to define indicators to compare the safety levels of different cycling routes between a specific origin and destination. Secondly, it discusses how these indicators can be
applied by road authorities in order to assess and improve the safety of cycling routes. And finally, it aims to discuss the function of different types of infrastructure in the cycling network, as elements in cycling routes.
... This concern is at odds with the evidence-base on contraflow cycling. Allowing contraflow cycling on one-way streets does not increase road traffic crashes (Alrutz et al., 2002;Ryley and Davies, 1998;Vandenbulcke et al., 2014). Instead it has been shown to reduce cyclist crash risk (Chalanton and Dupriez, 2014;Vandenbulcke et al., 2014;UDV, 2016) and may reduce crash numbers, density and severity (Alrutz et al., 2002). ...
... Allowing contraflow cycling on one-way streets does not increase road traffic crashes (Alrutz et al., 2002;Ryley and Davies, 1998;Vandenbulcke et al., 2014). Instead it has been shown to reduce cyclist crash risk (Chalanton and Dupriez, 2014;Vandenbulcke et al., 2014;UDV, 2016) and may reduce crash numbers, density and severity (Alrutz et al., 2002). Contrary to the opinion expressed above, conflicts and crashes have been shown to be greater for cyclists travelling with motor vehicle flow on one-way streets rather than contraflow (Alrutz et al., 2002;Chalanton and Dupriez, 2014) whilst motorists have been shown to reduce vehicle speed when encountering contraflow cyclists on narrow one-way streets and increase speeds as the road widens (Alrutz et al., 2002;UDV, 2016). ...
... Our findings corroborate existing evidence suggesting that there is no increase in crash risk when contraflow cycling is introduced on oneway streets (Vandenbulcke et al., 2014;Chalanton and Dupriez, 2014;UDV, 2016). It may even be true that the crash rate falls when contraflow cycling is introduced. ...
Contraflow cycling on one-way streets is a low cost intervention that research shows can improve the cycling experience and increase participation. Evidence from several studies suggest that cyclists on contraflows have a lower crash risk. However, implementing contraflow cycling is often controversial, including in the United Kingdom (UK). In this paper we examine whether contraflow cycling on one-way streets alters crash or casualty rates for pedal cyclists. Focusing on inner London boroughs between 1998 and 2019, we identified 508 road segments where contraflow cycling was introduced on one-way streets. We identified road traffic crashes occurring within 10 m of these segments and labelled them as pre-contraflow, contraflow or contraflow removed crashes. We calculated rates using the number of crashes or casualties divided by the time exposed and generated 95 % confidence intervals using bootstrap resampling. We adjusted the rates for changes in cordon cycling volume and injury severity reporting. There were 1498 crashes involving pedal cyclists: 788 pre-contraflow, 703 contraflow and 7 following contraflow removal. There was no change in adjusted overall pedal cyclist crash or casualty rates when contraflow cycling was introduced. Proximity to a junction doubled the crash rate. The crash rate when pedal cyclists were travelling contraflow was the same as those travelling with flow. We have found no evidence that introducing contraflow cycling increases the crash or casualty rate for pedal cyclists. It is possible that such rates may indeed fall when contraflow cycling is introduced if more accurate spatio-temporal cycling volume data was available. We recommend all one-way streets are evaluated for contraflow cycling but encourage judicious junction design and recommend UK legislative change for mandatory-two-way cycling on one-way streets unless exceptional circumstances exist.
... Consequently, a rich body of research has been geared toward identifying individual and infrastructural factors that contribute to the risk of cycling crashes (e.g. Aldred, 2016;Prati et al., 2017Prati et al., , 2018Vandenbulcke et al., 2014). Among the identified factors increasing injury risks are a high traffic volume, higher speed limits, and the complexity of junctions (Aldred et al., 2018;Vandenbulcke et al., 2014;Winters et al., 2012). ...
... Aldred, 2016;Prati et al., 2017Prati et al., , 2018Vandenbulcke et al., 2014). Among the identified factors increasing injury risks are a high traffic volume, higher speed limits, and the complexity of junctions (Aldred et al., 2018;Vandenbulcke et al., 2014;Winters et al., 2012). In contrast, both cycling tracks (Lusk et al., 2011;Thomas and DeRobertis, 2013) and cycling lanes (Park et al., 2015;Pulugurtha and Thakur, 2015) are considered highly effective in reducing crash risks. ...
... 2). In contrast, both cycling tracks (Lusk et al., 2011;Thomas and DeRobertis, 2013) and cycling lanes (Park et al., 2015;Pulugurtha and Thakur, 2015) are considered highly effective in reducing crash risks. (In contrast, there are also findings suggesting that dedicated cycling infrastructure can decrease cyclists' safety, for example Jensen, 2008. Vandenbulcke et al., 2014 higher crash rates at some intersection types featuring bikeways.) ...
There is ample evidence that adequate cycling infrastructure increases cyclists' safety. There is less research to what extent the specific design of cycling lanes affects subjective safety. We address this question by analysing data from a large-scale online survey, where participants rated images illustrating a wide range of cycling infrastructure designs for the anticipated level of subjective safety when imagining to cycle at the displayed location. Cycling tracks are perceived as safer than cycling lanes, which in turn are preferred over cycling on the street. Physical separations from the car lane, a greater lane width, and a coloured surface contribute most to a high subjective safety of cycling lanes. Additional buffers on the left-and right side of cycling lanes can have varying effects. On narrower cycling lanes, people experience extensive buffer designs as rather constraining and as impairing their safety. Combining several safety features (i.e. a sufficient demarcation of the left buffer and a coloured surface) is not necessarily beneficial for subjective safety. Our findings are mostly in line with findings on the factors benefitting or impairing objective safety. However, the relation of subjective and objective safety requires further attention.
... In order to make cycling safer, numerous studies have investigated factors increasing the crash risk of cyclists (e.g. Aldred, 2016;Prato et al., 2016;Vandenbulcke et al., 2014). However, next to the crash probability (i.e. an indicator of objective risk), there is also the subjective risk perception of cyclists. ...
... This level of 'objective risk' can be measured by the probability of being involved in a crash on a certain route. Among the factors that have been identified as increasing crash risks are a high traffic volume, higher speed limits, and the complexity of junctions (Aldred et al., 2018;Vandenbulcke et al., 2014;Winters et al., 2012). Further factors that have been discussed are, for example, tunnels and bridges, tram tracks, and public transport stops (see Vandenbulcke et al., 2014, for a review). ...
... Intersections remain objectively dangerous for cyclists even when we adjust for cycling volume (in line with, for example, Aldred et al., 2018;Vandenbulcke et al., 2014), and irrespective of the availability of cycling infrastructure. Similarly, the underestimation of the high crash risk at intersections without cycling infrastructure persist. ...
Most research concerned with cyclists' safety has been focused on the crash risk (i.e. their objective safety). However, there has been a growing interest in the perceived level of this risk (i.e. the subjective safety of cyclists). Crash risk and subjective risk perception in urban cycling appear to be mostly well aligned. For example, reduced speed limits have been found to reduce both objective and subjective risks (although there is also evidence for some incongruences). This absolute number of incidents could be misleading, as it does not account for potential differences in cycling volume (i.e. cyclists are likely to prefer streets with reduced speed limits). Thus, it may be important to adjust the absolute number of incidents relative number to the local cycling volume. In this research, we investigate the relation of cycling crashes and subjective risk perception (operationalized through reports from a crowd-sourcing project) for different types of cycling infrastructure and different speed limits, while accounting for the local cycling volume. We find that the absolute number of VGI reports and crashes can be misleading: whereas the absolute incident numbers, for example, suggest few benefits of cycling lanes and tracks, adjusting for the cycling volume reveals an increase of both objective and subjective safety as compared to streets without cycling infrastructure. We also identify situations where cyclists apparently underestimate the crash risk (i.e. on cycleways opposing the cars' traveling direction, and at streets with a speed limit of 30 km/h intersecting streets with higher speed limits). Additional research is required to understand the sources of these discrepancies, and how to make cyclists aware of them.
... Lorsque les investissements consacrés aux aménagements cyclables sont limités, les planificateurs et les décideurs doivent avant tout privilégier la mise en place d'infrastructures de haute qualité (continues, visibles et bien entretenues)plutôt que d'investir dans un vaste réseau construit à la hâte et sans précaution. Si les infrastructures cyclables sont mal conçues, elles pourraient avoir des effets néfastes au lieu de réduire les risques d'accident(Vandenbulcke et al., 2014).Comme démontré parVandemeulebroek et al. (2017) et Vandenbulcke et al. (2014, les SUL ne sont pas plus accidentogènes que les voiries " classiques ". Cependant, selonVandenbulcke et al. (2014), il convient de faire très attention lors de la conception des SUL, car ils sont associés à un risque accru d'accidents aux intersections. ...
... Lorsque les investissements consacrés aux aménagements cyclables sont limités, les planificateurs et les décideurs doivent avant tout privilégier la mise en place d'infrastructures de haute qualité (continues, visibles et bien entretenues)plutôt que d'investir dans un vaste réseau construit à la hâte et sans précaution. Si les infrastructures cyclables sont mal conçues, elles pourraient avoir des effets néfastes au lieu de réduire les risques d'accident(Vandenbulcke et al., 2014).Comme démontré parVandemeulebroek et al. (2017) et Vandenbulcke et al. (2014, les SUL ne sont pas plus accidentogènes que les voiries " classiques ". Cependant, selonVandenbulcke et al. (2014), il convient de faire très attention lors de la conception des SUL, car ils sont associés à un risque accru d'accidents aux intersections. ...
... Si les infrastructures cyclables sont mal conçues, elles pourraient avoir des effets néfastes au lieu de réduire les risques d'accident(Vandenbulcke et al., 2014).Comme démontré parVandemeulebroek et al. (2017) et Vandenbulcke et al. (2014, les SUL ne sont pas plus accidentogènes que les voiries " classiques ". Cependant, selonVandenbulcke et al. (2014), il convient de faire très attention lors de la conception des SUL, car ils sont associés à un risque accru d'accidents aux intersections. L'utilisation de logos de bicyclettes peints à l'entrée des rues SUL pourrait être utile pour avertir les automobilistes de la présence de cyclistes(Vandenbulcke et al., 2014).Enfin, signalons qu'un système efficace et interactif de signalement des défauts de la voirie ou des situations potentiellement dangereuses (y compris les encombrements sur les cheminements cyclistes) est disponible, tant pour les voiries régionales que communales : il s'agit de l'application Fix My Street (voir encadré dans le chapitre 6).7.4.3. ...
Cahiers de l'Observatoire de la mobilité de la Région Bruxelles-Capitale, 2020
Ce septième Cahier vient compléter la collection des Cahiers de l’Observatoire de la mobilité de la Région de Bruxelles-Capitale (RBC). Après avoir traité de l’offre de transport, des pratiques de déplacement en général et de celles liées au travail et à l’école en particulier, de logistique et de transport de marchandises, ou encore de partage de l’espace public entre tous les modes, ce nouveau Cahier s’arrête pour la
première fois sur un mode spécifique : le vélo. Cette publication comporte trois parties.
La première offrira une brève histoire du vélo racontée depuis Bruxelles et évitera d’emblée toute naturalisation du phénomène : le lent déclin du vélo au cours de la seconde moitié du 20e siècle résulte d’évolutions structurelles et non d’explications selon lesquelles Bruxelles ne serait " pas faite pour le vélo ". Cette première partie comportera également une mise en contexte institutionnelle afin d’identifier qui sont les acteurs compétents en matière de politique cycliste et la place occupée par celle-ci dans les outils réglementaires et planologiques régionaux, ainsi que dans ses budgets. Elle se terminera par une définition et une typologie des vélos et autres engins de déplacement légers.
La deuxième partie du Cahier abordera la pratique du vélo en RBC à travers une analyse approfondie du parc vélo et des déplacements à vélo.
Enfin, la troisième partie analysera la cyclabilité de la Région : les aménagements pour le vélo en mouvement, la sécurité et l’insécurité des cyclistes, le stationnement des élos et les services liés au vélo.
Une conclusion générale viendra clore ce vaste exercice de synthèse.
À noter que les données mobilisées dans ce Cahier ont été arrêtées en juillet 2019. Il va de soi qu’une actualisation régulière de cette synthèse sera nécessaire pour suivre l’évolution de ce secteur en pleine ébullition.
... Hierarchical Bayes-Spatial Model According to Vandenbulcke, Thomas, and Panis (2014), due to the increasing trend of using bicycles as a transportation mode, an APM for cyclists is of great importance in order to make the transport system safer for cyclists. Cycling is better for people's health with the aspect of getting exercise. ...
... When there is a lack of information for the values of the parameters a and b, the values are in general specified as a=0 and b=1,1 -6 . However, according to Vandenbulcke, Thomas, and Panis (2014), the hidden statistics for cyclists in accidents are relatively high since many people do not bother to report the accidents to the police. ...
... So as the dependent variable is binary, a conditional Bernoulli with a logistical link (2 staged formula as equation 3.3, 3.4) is used inorder to find the probability of bicycle accident at a particular location. Where the equation 3.3 is an accident risk model followed by logit distribution (Vandenbulcke;Thomas;and Panis , 2014). ...
The growing population and motorization generate more movements. In many cities, the increase of population and motorization is much greater than the development of the capacity of the transportation network. For unprotected road users, the risk of getting in a traffic accident increases and the risk of being more severely injured in an accident. In March 2020, a pandemic was declared because of a Coronavirus. More people started to work/study from home to prevent the virus from spreading by avoiding unnecessary trips, gatherings, and crowded areas. Therefore, travel behaviours have shifted during the pandemic compared to previous years. This project aims to get knowledge of how mobility and traffic accidents are affected by significant shifts of travel flow, predict the effect of traffic accidents based on mobility, and evaluate the risk of travelling on a particular road segment.
Mobility data has been collected from Google Mobility, Apple Mobility, the Environmental Barometer, Trafikkontoret, and traffic accident data collected from STRADA. Mobility and traffic accident data have been analysed using Excel, QGIS, and PTV Visum Safety. The accident rates have been calculated to determine if the accident rate has changed during the pandemic, and three scenarios for 2021 have been predicted. A risk analysis model has also been used to calculate the risk of being involved in an accident on particular streets using a car, cycle, or walking.
It was found that mobility has decreased, and the usage of transportation modes has shifted. During the pandemic, it has been more popular to cycle, which is also reflected in the traffic accident data, where the percentages of cyclists being involved in traffic accidents have increased. No matter the degree of injury or transportation mode, the total number of traffic accidents had decreased in 2020. However, the number of severe accidents is almost the same as in previous years. Males are overrepresented in traffic accidents, and the differences are even more significant for 2020. In 2020, the travel speed on the roads increased, which might be due to decreased traffic volume, making it possible to drive faster. The percentage of accidents involving alcohol also increased. The results from the risk analysis show whether the risk of getting into a traffic accident on a particular street using a specific transportation mode has increased or decreased depending on the street and transportation mode. Three scenarios (better, same, worse) of the risk of travelling on the particular road stretches in 2021 have also been calculated.
To make better predictions, additional years should be considered. For future work, it would also be interesting to consider the weather since it greatly impacts the transportation mode used and the risk of accidents. The infrastructure was not considered in this project, which would be interesting since the transportation mode, route used, and speed might depend on the infrastructure and current constructions.
... Despite this high number, only a handful of studies mention this type of accidents. Vandenbulcke et al. (2014) already found the presence of on-road tram tracks and (tracks) crossings to be related with an increased risk of accident (Vandenbulcke et al. 2014). Only one published study in the UK, performed by Maempel et al. (2018), regarding patient characteristics and type of sustained injuries, was found (Maempel et al. 2018). ...
... Despite this high number, only a handful of studies mention this type of accidents. Vandenbulcke et al. (2014) already found the presence of on-road tram tracks and (tracks) crossings to be related with an increased risk of accident (Vandenbulcke et al. 2014). Only one published study in the UK, performed by Maempel et al. (2018), regarding patient characteristics and type of sustained injuries, was found (Maempel et al. 2018). ...
... On-road tram tracks are known to be related with an increased risk of accident (Vandenbulcke et al. 2014). Studies conducted in Amsterdam have shown that identifying accident "hotspots" and separating trams from other vehicles and bicycles in these areas, may lead to a significant reduction in accidents (Danish Transportation Council 1995). ...
Objective
There is a paucity of data on the incidence and severity of tram tracks related cycling injuries. The aim of this study is to get insight into the incidence, severity and characteristics of tram tracks related cycling injuries, potentially defining significant ‘hotspots’ in the Ghent city area.
Methods
A one-year, multicenter, prospective, observational study was conducted. Patients presenting to the emergency departments of all 4 Ghent hospitals with tram tracks related cycling injury, were included. Data on patient demographics, circumstances of the accident and type of injury were collected.
Results
149 patients were included, with a median age of 31 years. 42 patients had fractures, 39 patients required wound sutures, 79 and 49 patients suffered from bruising and abrasions respectively. Only 5 patients required admission. No patients died or suffered life-threatening injuries. Women (65.1%) presented more frequently than men (34.9%). Forty-tree percent of all accidents happened in autumn (p < 0.001). Accidents happened more frequently in rainy conditions (p < 0.001). Mean number of days off work was 2.7 days, significantly increasing to 6.56 days when sustaining a fracture or dislocation (p = 0.02).
Conclusion
Tram tracks are potentially dangerous and may lead to clinically important injuries and significant number of days off work.
... The implementation of bicycle lanes not only attracts bicycle usage but also decreases the number of bicycle crash incidents (Bacchieri et al., 2010;Chen et al., 2012;Dhakal et al., 2018). As for intersections, high intersection density is significantly associated with bicycle crash frequency (Siddiqui et al., 2012;Strauss et al., 2013;Wei and Lovegrove, 2012;Vandenbulcke et al., 2014). Wang et al. (2019) found that urban bridges were identified as high-risk areas of bicycle-vehicle collisions. ...
... In relation to socio-demographics, the population and employment densities are significantly associated with bicycle crash frequency (Siddiqui et al., 2012), whereas these variables are insignificant in Chen's work (2015). As for land use factors, areas with high percentage of commercial and industrial development increase the likelihood of injuries among cyclists (Narayanamoorthy et al., 2013;Vandenbulcke et al., 2014;Cai et al., 2016). However, Strauss et al. (2012) found that the percentage of commercial land use is not an influential explanatory variable for bicycle crash incidents. ...
... Negative binomial (NB) regression as the basic method is applied earlier due to its ability of handling over-dispersion issue (Wei and Lovegrove, 2013;Pulugurtha and Sambhara, 2011). Thereafter, more spatial statistical models have emerged for pedestrian crash analysis with consideration of spatial autocorrelations (Chen, 2015;Vandenbulcke et al., 2014;Chen and Zhou, 2016;Wang and Kockelman, 2013). Considering the randomness of traffic accidents, random effects models with Poisson distribution have become increasingly popular (Aziz et al., 2017;Ukkusuri et al., 2011;Jiang et al., 2014). ...
Bicycle wrong way riding (WWR) is a dangerous and often neglected behavior that engenders threats to traffic safety. Owing to the lack of exposure data, the detection of WWR and its relationship with the built environment (BE) factors remain unclear. Accordingly, this study fills the research gaps by proposing a WWR detection framework based on bike-sharing trajectories collected from Chengdu, China. Moreover, this study adopts Negative Binomial-based Additive Decision Tree to investigate the impacts of built environment on WWR frequencies. Results reveal that (1) WWR distribution is unaffected by different periods in a day; (2) road length is more influential than road level and road direction in WWR occurrence; (3) company, bus stop, subway station, residence, and catering facility are primary contributors affecting WWR behavior during peak hours, whereas education becomes an emerging influential variable during nonpeak hours; and most importantly, (4) these variables clearly present non-linear effects on the WWR frequencies. Therefore, geographically differentiated policies should be adopted for bicycle safety improvement.
... One major deterrent to cycling is the perceived risk of crashes (Aldred, 2016;Parkin et al., 2007;Winters et al., 2011), which appears justified in light of the higher crash rates on bike trips as compared to car trips (Winters et al., 2012). Consequently, a rich body of research has been geared toward identifying individual and infrastructural factors that contribute to the risk of cycling crashes (e.g., Aldred et al., 2018;Prati et al., 2017Prati et al., , 2018Vandenbulcke et al., 2014). ...
... The level of 'objective risk' can be measured through the probability of being involved in a crash on a certain route. Among the identified factors increasing injury risks are a high traffic volume, higher speed limits, and the complexity of junctions (Aldred et al., 2018;Vandenbulcke et al., 2014;Winters et al., 2012). Further factors that have been discussed are, for example, tunnels and bridges, tram tracks, and public transport stops (see Vandenbulcke et al., 2014, for a review). ...
... Among the identified factors increasing injury risks are a high traffic volume, higher speed limits, and the complexity of junctions (Aldred et al., 2018;Vandenbulcke et al., 2014;Winters et al., 2012). Further factors that have been discussed are, for example, tunnels and bridges, tram tracks, and public transport stops (see Vandenbulcke et al., 2014, for a review). In contrast, cycling tracks are considered to be highly effective in reducing crash risks (Thomas and DeRobertis, 2013). ...
Background
Urban planners frequently neglect the role of subjective risk perception during urban cycling. Several findings suggest a complex relationship between the risk of being involved in a crash and the subjective anticipation of this risk.
Data collection and methods
We investigate the relation of objective risks (operationalized through crashes involving cyclists) and subjective risk perception (operationalized through citizens’ reports in a crowdsourcing project) in a medium-sized German city. Using GIS methods, these datasets are linked to various infrastructure and traffic properties that have been found relevant for cycling safety.
Results
Despite a generally high alignment of objective and subjective risk, our findings highlight that the subjective risk perception at a given location can deviate significantly from the actual crash risk. For example, the subjective perception of high risk on one-way streets with bikeways in opposing direction is not matched by a high level of objective risk. Vice versa, some rather dangerous situations (e.g., tram stops) are not perceived as particularly dangerous.
Conclusions
Understanding why and where cyclists over- or underestimate the actual crash risk may provide a foundation for the design of safer cycling infrastructures, as well as for promoting cycling as a comfortable mode of transportation.
... Most incidents in protected bike lanes involving pedestrians in this study occurred midblock, which can result from pedestrians using the lane for travel, crossing midblock, exiting a vehicle parked adjacent to it, or waiting for a taxi or other vehicle. It is unclear why protected bike lanes may be more susceptible to pedestrian obstructions than conventional bike lanes, but possibilities include that they can stand between pedestrians exiting parked cars and the sidewalk (Vandenbulcke et al., 2014) or that some pedestrians treat protected bike lanes as sidewalks because they are buffered from traffic. There were no crashes or falls due to pedestrians on the protected bike lanes with heavy separation, and the more substantial barriers and fewer intersections on these facilities likely gave pedestrians fewer openings to enter and cross them. ...
... Crashes with vehicles in protected bike lanes occurred less often in New York, where there were no alleys and most lanes were seldom intersected by driveways. Increased density of junctions increase the risk of bicyclist-motor vehicle crashes because they introduce additional opportunities for conflict (Li et al., 2017;Pulugurtha and Thakur, 2015;Siddiqui et al., 2012;Vandenbulcke et al., 2014;Wei and Lovegrove, 2013). Protected bike lanes with heavy separation in this study had fewer junctions than those with light separation, which likely contributed to their lower risk. ...
... Local streets with and without bicycle facilities or traffic calming were associated with low crash or fall risks (Aldred et al., 2018;Minikel, 2012), and downhill grade increased risk (Allen-Munley et al., 2004;Klop and Khattak, 1999), likely because it increased cyclist speed. Streetcar or train tracks increased risk substantially, which is consistent with findings from Toronto, Vancouver, and Brussels (Teschke et al., 2016;Vandenbulcke et al., 2014) and should be a consideration for cities expanding or implementing a streetcar network. ...
Objective
Protected bike lanes separated from the roadway by physical barriers are relatively new in the United States. This study examined the risk of collisions or falls leading to emergency department visits associated with bicycle facilities (e.g., protected bike lanes, conventional bike lanes demarcated by painted lines, sharrows) and other roadway characteristics in three U.S. cities.
Methods
We prospectively recruited 604 patients from emergency departments in Washington, DC; New York City; and Portland, Oregon during 2015–2017 who fell or crashed while cycling. We used a case-crossover design and conditional logistic regression to compare each fall or crash site with a randomly selected control location along the route leading to the incident. We validated the presence of site characteristics described by participants using Google Street View and city GIS inventories of bicycle facilities and other roadway features.
Results
Compared with cycling on lanes of major roads without bicycle facilities, the risk of crashing or falling was lower on conventional bike lanes (adjusted OR = 0.53; 95 % CI = 0.33, 0.86) and local roads with (adjusted OR = 0.31; 95 % CI = 0.13, 0.75) or without bicycle facilities or traffic calming (adjusted OR = 0.39; 95 % CI = 0.23, 0.65). Protected bike lanes with heavy separation (tall, continuous barriers or grade and horizontal separation) were associated with lower risk (adjusted OR = 0.10; 95 % CI = 0.01, 0.95), but those with lighter separation (e.g., parked cars, posts, low curb) had similar risk to major roads when one way (adjusted OR = 1.19; 95 % CI = 0.46, 3.10) and higher risk when they were two way (adjusted OR = 11.38; 95 % CI = 1.40, 92.57); this risk increase was primarily driven by one lane in Washington. Risk increased in the presence of streetcar or train tracks relative to their absence (adjusted OR = 26.65; 95 % CI = 3.23, 220.17), on downhill relative to flat grades (adjusted OR = 1.92; 95 % CI = 1.38, 2.66), and when temporary features like construction or parked cars blocked the cyclist’s path relative to when they did not (adjusted OR = 2.23; 95 % CI = 1.46, 3.39).
Conclusions
Certain bicycle facilities are safer for cyclists than riding on major roads. Protected bike lanes vary in how well they shield riders from crashes and falls. Heavier separation, less frequent intersections with roads and driveways, and less complexity appear to contribute to reduced risk in protected bike lanes. Future research should systematically examine the characteristics that reduce risk in protected lanes to guide design. Planners should minimize conflict points when choosing where to place protected bike lanes and should implement countermeasures to increase visibility at these locations when they are unavoidable.
... At the same time that the promotion of bicycle traffic as a part of green mobility has been actively implemented in cities worldwide (Gössling, 2013;Holm et al., 2012), bicycle-related collisions have increasingly caused casualties and property damage (Lee & Abdel-Aty, 2017;Kaplan et al., 2014;Stipdonk & Reurings, 2012;Vandenbulcke-Plasschaert, 2011;Lusk et al., 2011). Collision risks deter people from cycling (Vandenbulcke et al., 2014). Compared to driving cars, cyclists have a higher injury risk in road collisions (Sanders, 2015;Reynolds et al., 2009). ...
... Compared to driving cars, cyclists have a higher injury risk in road collisions (Sanders, 2015;Reynolds et al., 2009). Unfortunately, the risks of bicycle collisions are insufficiently known (Vandenbulcke et al., 2014) because calculations of risk exposure are based on general data (e.g. census/population data) (Schepers et al., 2014;ITF & OECD, 2013) or inadequate data (e.g. ...
... This may be due to sudden changes in road environments (e.g. narrowed lanes, tilted slopes, sharp curvatures, lower visibility of riders, etc.) (Vandenbulcke et al., 2014;Wang & Nihan, 2004). In Case 2, arterial roads are identified as black spots due to complex traffic situations in road environments (e.g. ...
... This is because higher MXI leads to shorter travel distances which results in reduced car-use and consequently risk exposure specifically for the cyclists (Chen & Shen, 2016;Schepers et al., 2019). Proximity to facilities such as transit stops, commercial and service locations, on the other hand, were shown to increase the risk of crashes that involve vehicles and/or bicycles (Kim et al., 2010;Osama & Sayed, 2017;Schepers, 2021b;Vandenbulcke et al., 2014;Wei & Lovegrove, 2013). However, Kim et al. (2011) suggested that increased proximity to roads is associated with reduced crash severity. ...
... This result seems to contradict the findings of Van Petegem et al. (2021) and other previous studies showing that separated cycle paths are usually safer than cycle lanes adjacent to 50 km/h streets and mixed traffic streets (Adams & Aldred, 2020;Aldred et al., 2021). Although the separated cycle paths prevent conflict with motor-vehicles, the presence of busy side roads, parked vehicles besides the cycle paths, as well as large intersections that need to be crossed can increase the probability and frequency of V&B crashes (Vandenbulcke et al., 2014). ...
Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m² cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.
... To tackle the above problems, safety engineers, transport agencies, and researchers have investigated various aspects of bicycle accidents to identify the factors associated with bicycle crash occurrence (Boele-Vos et al., 2017;Janstrup et al., 2019;Aldred et al., 2018;Vandenbulcke et al., 2014;Dozza, 2017;Rossetti et al., 2018;Twisk and Reurings, 2013;Morrison et al., 2019;Kaplan and Giacomo Prato, 2015;Ji et al., 2021;Saha et al., 2018;Raihan et al., 2019), and injury outcome (Myhrmann et al., 2021;Kaplan et al., 2014;Fountas et al., 2021;Kim et al., 2007;Behnood et al., 2014;Thomas and DeRobertis, 2013;Chen et al., 2017;Samerei et al., 2021) to make informed mitigating efforts. However, as highlighted by Dozza (2017) and Thomas and DeRobertis (2013), many such investigations do not account for cyclist exposure. ...
... One such task is to improve cycling safety, which involves accident analysis. As previous studies have highlighted the necessity of accounting for exposure in accident analysis (Vandenbulcke et al., 2014;Thomas and DeRobertis, 2013;Aldred et al., 2018;Norros et al., 2016), we wish to quantify the impact of the quality of the exposure data further. ...
Cycling can reduce greenhouse gas emissions and air pollution and increase public health. With this in mind, policy-makers in cities worldwide seek to improve the bicycle mode-share. However, they often struggle against the fear and the perceived riskiness of cycling. Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. This requires the analysis of the factors surrounding accidents and the outcome. However, meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available at a segment level. Therefore, safety engineers often rely on aggregated variables or calibration factors that fail to account for variations in the cycling traffic caused by external factors. This paper fills this gap by presenting a Deep Learning based approach, the Long Short-Term Memory Mixture Density Network (LSTMMDN), to estimate hourly bicycle flow in Copenhagen, conditional on weather, temporal and road conditions at the segment level. This method addresses the shortcomings in the calibration factor method and results in 66-77\% more accurate bicycle traffic estimates. To quantify the impact of more accurate bicycle traffic estimates in cycling safety analysis, we estimate bicycle crash risk models to evaluate bicycle crashes in Copenhagen. The models are identical except for the exposure variables being used. One model is estimated using the LSTMMDN estimates, one using the calibration-based estimates, and one using yearly mean traffic estimates. The results show that investing in more advanced methods for obtaining bicycle volume estimates can benefit the quality, mitigating efforts by improving safety analyses and other performance measures.
... Furthermore, as the 50 km/h roads in this study are distributor roads, they attract more cyclists and motorised vehicles compared to 30 km/h roads and have more connections to other streets. This leads to more complexity due to more cross overs by cyclists and more turning vehicles, resulting in increased encounters between cyclists and motorised vehicles (Vandenbulcke et al., 2014). Aldred et al. (2018) had similar results: they argue that residential roads are safer for cyclists compared to other road types due to lower motorised vehicle volumes, higher bicycle volumes and lower speed limits. ...
... An explanation for this result might be that 50 km/h roads with separated bicycle facilities attract more cyclists and motorised vehicles, which eventually leads to more bicycle crashes. Additionally, crossing over distributor roads from a separated bicycle facility is indicated as a risk increasing factor by Vandenbulcke et al. (2014). On 30 km/h roads with separated bicycle facilities, none of the coefficients are significant, meaning that no relationship was found between exposure and bicycle crashes. ...
Cycling is promoted as a sustainable and healthy mode of transport, which results in an increase in bicycle use in urban areas. Increasing bicycle use comes with growing concerns about cyclist safety. This study examines how the temporal variation in the network-wide exposure to cyclists and motorised vehicles affects bicycle crash frequency. Network-wide hourly volumes of cyclists and motorised vehicles were estimated and regression models were used to identify the effect of the exposure to traffic on bicycle crashes in the city of Utrecht, a Dutch cycling capital. The results show that increasing exposure to motorised vehicles, and to a lesser extent, exposure to cyclists, increases the number of bicycle crashes on 50 km/h roads. For 30 km/h roads, no statistically significant relationship between the exposure to cyclists and bicycle crashes was found. Moreover, it was shown that cyclist crash numbers on 30 km/h roads are less sensitive to an increase in the exposure to motorised vehicles compared to cyclist crash numbers on 50 km/h roads. Furthermore, the exposure to motorised vehicles is a stronger factor affecting the increase in bicycle crashes on roads with bicycle lanes or mixed traffic conditions than on roads with separated bicycle facilities. To conclude, this study shows that road safety for cyclists needs further improvements, as cycling in cities keeps increasing.
... Extracted contributory factors were then grouped thematically where appropriate. For example, specific features of the road environment identified as crash contributory factors, such as a narrow apron (Hels & Orozova-Bekkevold, 2007), width and height of roundabout centre island (Jensen, 2017), number of lanes (Akgün, Dissanayake, Thorpe, & Bell, 2018;Robartes & Chen, 2017), and bridges without cycle paths (Vandenbulcke, Thomas, & Int Panis, 2014), were placed within a generic 'Road environment' contributory factor. ...
... As shown in Fig. 5, the majority of studies identified contributory factors relating to the road environment (n = 84) and cycling infrastructure (n = 45). In this context, road environment refers to features or structure of the road, such as the height or width of a centre island (Jensen, 2017), or the slope/curvature of the road , whereas cycling infrastructure refers to the structure or features of the cycling-specific environment, such as the presence of a pedestrian crossing over the cycling lane (Poulos et al., 2015) or whether the cyclist was on a path which was marked or unmarked (Vandenbulcke et al., 2014). Other studies identified contributory factors related to different aspects of cyclist behaviour, including loss of control (n = 14), failing to give way (n = 8), distraction (n = 5), obstructed view (n = 5) and direction of travel (n = 5). ...
There is a growing body of road safety research that seeks to identify crash contributory factors beyond road users, their vehicles, and the immediate road environment. Although cyclist safety represents a critical research area, this ‘systems thinking’ approach has received less attention in bicycle crash analysis. This article presents the findings from a systematic literature review which aimed to synthesise the peer reviewed literature regarding bicycle crash contributory factors (defined as factors which play a contributory role in bicycle crashes, as opposed to risk factors which are factors which may increase the probability of crashes). Crash contributory factors were extracted from included articles and mapped onto a systems thinking framework comprising seven hierarchical road transport system levels. The findings show that a majority of the included studies identified contributory factors relating to the road environment, cycling infrastructure, and cyclist and driver behaviour. No studies identified contributory factors outside of cyclists and road users, bicycles and vehicles, and the road environment and few specifically examined causal relationships between contributory factors. It is concluded that there are gaps in the knowledge base regarding the broader transport system features that play a role in bicycle crashes and how contributory factors interact to create crashes. We argue that more expansive research into the systemic factors involved in bicycle crashes is required and that initial work should focus on the development of new data sources and analysis methods.
... Based on black spot identification results, optimization measures can be accurately implemented by traffic engineers. The traditional method is to calculate the number or rate of traffic accidents on a road segment [16]. If the statistic value on a road segment is greater than the pre-defined threshold, it is considered to be an accident black spot [14]. ...
With increasing numbers of crashes and injuries, understanding traffic accident spatial patterns and identifying blackspots is critical to improve overall road safety. This study aims at detecting blackspots using optimized hot spot analysis (OHSA). Traffic accidents were classified by their participants and severity to explore the relationship between blackspots and different types of accidents. Based on the outputs of incremental spatial autocorrelation, OHSA was then implemented on different types of accidents. Finally, the performance of OHSA in evaluating the road safety level of the proposed RBT index are examined using a binary correlation analysis (i.e., R2 = 0.89). The results show that: (1) The optimal scale distance varies from 0.6 km to 2.8 km and is influenced by the distance of the travel mode. (2) Central cities, with 54.6% of the total accidents, experiences more rigorous challenges regarding traffic safety than satellite cities. (3) There are many types of black spots in vulnerable communities, but in some specific areas, there are only black spots of non-motor vehicle accidents. Considering the practical significance of the above results, policy makers and traffic engineers are expected to give higher attention to central cities and vulnerable communities or prioritize the implementation of relevant optimization measures.
... The probability of a cycling crash is much higher at intersections as along the road (e.g. [1], [2]). A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. ...
... Geographic information system (GIS) is a tool used for traffic crash analysis; it considers several parameters, namely the number of fatalities and property damage in road traffic crashes 29,30 . GIS helps in the spatial analysis and identification of 'black spots' zones 31,32 . The most common method used for spatial analysis in a GIS environment is kernel density estimation (KDE) 33,34 . ...
The increase in traffic volume in urban road networks poses a significant challenge to transportation safety. It is evident that different traffic zones experience unique crash patterns and severities. The different factors that affect crash rates are caused by the various characteristics of the drivers, weather conditions, design of roadside infrastructure and driving behaviour. Although studies have shown that various factors can affect crash rates, there are insufficient studies on the exact catego-rization of these factors. Accordingly, the present study focuses on traffic crashes on streets where the risks of an accident occurrence are higher, using Nagpur city, Maharashtra, India as a case study. Three levels of risk zones were selected, i.e. zone-I (low risk), zone-II (medi-um risk) and zone-III (high risk). The risk zones are created in ArcGIS software using the kernel density esti-mator function. The association rule was then used to find out the various crash risk factors within the zone. The results of the study reveal that the risk of pedestrian fatalities is higher in areas where the speed limit is more than 40 km/h and day-today pedestrian activity is present. Based on the results, we propose a lower speed limit in zone-I, in addition to providing pedestrian-crossing facilities such as zebra crossings or refuge islands for cross-walks. Moreover, we propose implementing an awareness campaign for road traffic safety aimed at educating road users on how to follow road discipline, especially with regard to utilizing pedestrian facilities, aggressive young motorcyclists, lane changing and overtaking manoeuvres .
... The important parameter for the number traffic accidents reduction is the good visibility around the vehicle, that is the reduction of blind spots, which will reduce the possibility of a traffic accidents occurrence (Pitchipoo et al. 2014). Research of Vandenbulcke et al. (2014) shows that the presence of truck, contributes to the great number of traffic accidents with bicyclists, and besides this, the accidents are Fig. 10 The case of the scooter driver visibility without application of the class VI mirror Fig. 9 The visual field of the truck driver with application of the class VI mirror Liu et al. 2017). The review of measures for the reduction of number of traffic accidents between the truck and vulnerable group of traffic participants, is shown in Table 3. traffic. ...
In the world of computers, that is, by application of modern tools to determine and represent the visual field, of the heavy-duty vehicles driver in the virtual environment, the real investigations can be successfully replaced with the virtual ones. Demands which one vehicle must to satisfy during the projecting phase are: comfort, visibility, easy manoeuvrability, esthetical demands and similar. One very important demand from the aspect of the safety of all traffic participants and reliability of all systems on the vehicle is the good visibility around the vehicle, which investigation is the aim of this paper. The purpose of this paper is the analysis of everyday situation of truck driver at the intersection, in the virtual reality, as well as the analysis of causes which lead to the traffic accident. The main aim of the paper is to determine do a truck driver sees the vulnerable group of traffic participants depending from their mutual position, by application of RAMSIS software. By application of the virtual reality, the main finding of this study, is that the truck driver in some situations cannot see the vulnerable group of traffic participants. The originality of this study bases on the investigation, do a truck driver sees the electric scooter driver, and the idea for such research have come on the basis of everyday situations, because the electric scooters are more and more present on streets.
Graphical Abstract
... Naast open-deurongevallen vinden ook ongevallen plaats tussen in-en uitparkerende motorvoertuigen en fietsers (Vandenbulcke, Thomas & Int Panis, 2014). In een Amerikaanse studie is met video-observaties op twee locaties gekeken naar fietsstroken langs parkeerstroken (Hunter & Stewart, 1999). ...
This research shows how parking lanes alongside distributor roads inside build up areas increases the risk on bicycle-motorvehicle crashes on road sections and intersections. The report is in Dutch, but an English summary is available.
The research questions are:
- Do parking facilities along distributor roads with bicycle tracks near distributor/access road intersections increase the risk of crashes between bicycles and motor vehicles?
- What distance to distributor/access road intersections can be considered acceptable or safe for parking facilities along distributor roads?
To answer the research question – and to gain more insight into what role parking facilities play in bicycle crashes in general – the following was undertaken:
- A literature study of the risks cyclists run due to parking along distributor roads in general and parking near intersections in particular.
- The development of a research database of intersection locations with the associated crash data, traffic data and infrastructural characteristics. This database is based on (open) geodata sources and GIS analyses to identify intersections, record intersection characteristics, and link the different data needed.
- Analysis of BRON-registered (Database of Registered Crashes in the Netherlands) bicycle crashes involving parked vehicles or parking manoeuvres.
- Development of Crash Prediction Models (CPMs) for three-legged distributor/access road intersections, with the risk factor being the distance of the parking facilities to the intersection.
... Naast open-deurongevallen vinden ook ongevallen plaats tussen in-en uitparkerende motorvoertuigen en fietsers (Vandenbulcke, Thomas & Int Panis, 2014). In een Amerikaanse studie is met video-observaties op twee locaties gekeken naar fietsstroken langs parkeerstroken (Hunter & Stewart, 1999). ...
Dit onderzoek laat zien op welke wijze parkeervoorzieningen tot een verhoogd risico op fiets-motorvoertuigongevallen leiden op wegvakken en kruispunten binnen de bebouwde kom. De onderzoeksvragen luidden als volgt:
- Verhogen parkeervoorzieningen langs GOW’s met fietspaden nabij GOW-ETW-kruispunten de kans op fiets-motorvoertuigongevallen?
- Wat is een acceptabele of veilige afstand van parkeervoorzieningen langs GOW’s tot GOW-ETW-kruispunten?
Voor de antwoorden op de onderzoeksvragen – en voor meer inzicht in de rol van parkeervoorzieningen bij fietsongevallen in het algemeen – zijn de volgende activiteiten uitgevoerd:
- Een literatuuronderzoek naar de risico’s van parkeren langs gebiedsontsluitingswegen voor fietsers in het algemeen en rond kruispunten in het bijzonder.
- De ontwikkeling van een onderzoeksdatabase van kruispuntlocaties met bijbehorende ongevallengegevens, verkeersgegevens en infrastructurele kenmerken. Deze database is gebaseerd op (open) geodatabronnen en GIS-analyses voor het identificeren van kruispunten, het vastleggen van kruispuntkenmerken en koppelen van de verschillende benodigde gegevens.
- Analyse van BRON (Bestand geRegistreerde Ongevallen in Nederland) op parkeerongevallen met fietsers: ongevallen waarbij geparkeerde voertuigen of parkeermanoeuvres een rol spelen.
- De ontwikkeling van Crash Prediction Models (CPM’s) van 3-taks GOW-ETW-kruispunten met de afstand van parkeervoorzieningen tot het kruispunt als risicofactor.
... The bicycle is considered a mode of transport that should be enhanced, as it complies with the principles of environmental, social and economic sustainability (Gössling, 2016). Efforts for efficient urban commuting by bicycle are unanimous in the scientific literature, which points out different ways of achieving this same goal, whether through speed (Ellison & Greaves, 2011), travel time (McNeil, 2011, security (Vandenbulcke et al., 2014), intermodality and sharing (Boullier & Crépel, 2014), service level (Lowry et al., 2012) or route preferences (Vedel et al., 2017). ...
In order to broaden the discussion on the safety of bicycle transport, this paper uses the analytical capacity of the spatial syntax applied to the recording of accidents involving cyclists in the city of Rolândia-PR, located in the Southern Region of Brazil. With the availability of 535 reports of trips made by bicycle mode, collected in the survey origin and destination of the city, a database was elaborated where each segment of the road received its numerical value from the loading of trips, and its corresponding values of choice and integration, generated in the Depthmap software. As a result, the relationship between the point of accident and the trip record by bicycle was refuted. In contrast, the angular values of choice and integration were sufficient to explain the occurrence of accidents involving cyclists in each segment of the municipal urban network, statistically proven through the generation of a generalized linear model. The contribution of this study focuses on the validity of using spatial syntax to predict safer routes, which is considered a theoreticalmethodological approach that identifies priority routes for the implementation of specific infrastructure for bicycle transport.
Keywords: Bikeability. Cyclability. Safety
... Several studies, for instance, have shown an increased risk of pedestrians and cyclist collisions at intersections compared to nonintersections [226][227][228][229][230]. Other research has revealed that road infrastructure features, such as lane width and road markings, can influence the risk of road accidents. For example, it was observed that when the width of the bike path increased the probability of bicycle accident decreased [231] while where there were no cycle facilities the risk of collision increased [232]. However, there have been disagreements about the possibility of separating motorized and nonmotorized traffic flows on different paths. ...
The most frequent justification for implementing automated vehicles is the claim that they will increase road safety by removing human involvement in driving. This, however, introduces emerging Human Factors (HFs) issues, since regardless of the level of automation, the human being will continue to play a crucial role in interacting with vehicle automation. In the medium-low levels, the driver will have to play a supervisory role which will introduce out-of-the-loop problems, in the driver-vehicle interaction during the transition of control. At the higher level, new forms of accidents may occur associated with the need for automated vehicles to interact with other road users. The chapter is a thorough literature review of the HFs for both of these interactions, mainly those relating to the medium-low level of automation. Such review is aimed at understanding the influences of HFs on road safety and the role played by infrastructures.
... Since crashes that occur close in space or time may share some unobserved characteristics, ignoring these spatial and temporal correlations in safety modeling may lead to biased estimates and loss of model power (Lord and Mannering, 2010;Mannering et al., 2016;Osama and Sayed, 2016;Savolainen et al., 2011). Many studies have addressed the spatial error correlation effects when modeling cyclist-vehicle crashes (Amoh- Gyimah et al., 2016;Cheng et al., 2017;Osama and Sayed, 2016;Saha et al., 2018;Siddiqui et al., 2012;Vandenbulcke et al., 2014;Zhang et al., 2012). On the other hand, there is a limited number of studies that addressed the temporal error correlation effects when modeling cyclistvehicle crashes (Ulak et al., 2018). ...
Crash data is usually aggregated over time where temporal correlation contributes to the unobserved hetero-geneity. Since crashes that occur in temporal proximity share some unobserved characteristics, ignoring these temporal correlations in safety modeling may lead to biased estimates and a loss of model power. Seasonality has several effects on cyclists' travel behavior (e.g., the distribution of holidays, school schedules, weather variations) and consequently cyclist-vehicle crash risk. This study aims to account for the effect of seasonality on cyclist-vehicle crashes by employing two groups of models. The first group, seasonal cyclist-vehicle crash frequency , employs four vectors of the dependent variables for each season. The second group, rainfall involved cyclist-vehicle crash frequency, employs two vectors of the dependent variables for crashes that occurred on rainy days and non-rainy days. The two model groups were investigated using three modeling techniques: Full Bayes crash prediction model with spatial effects (base model), varying intercept and slope model, and First-Order Random Walk model with a spatial-temporal interaction term. Crash and volume data for 134 traffic analysis zones (TAZ's) in the City of Vancouver were used. The results showed that the First-Order Random Walk model with spatial-temporal interaction outperformed the other developed models. Some covariates have different associations with crashes depending on the season and rainfall conditions. For example, the seasonal estimates for the bus stop density are significantly higher for the summer and spring seasons than for the winter and autumn seasons. Also, the intersection density estimate for a rainy day is significantly higher than a non-rainy day. This indicates that on a rainy day each intersection to the network adds more risk to cyclists compared to a non-rainy day.
... However, many studies have found a higher occurrence of crashes on bidirectional bike lanes than on unidirectional ones because drivers do not expect cyclists to ride on the right side of the road (Methorst et al., 2017). Therefore, crashes are more likely at intersections with bidirectional bike lanes (Schepers et al., 2011;Thomas and DeRobertis, 2013;Vandenbulcke et al., 2014). We then advise authorities to adopt appropriate countermeasures such as speed-reducing measures, removal of sight obstructions, or LED-lights on the surface along the crossing which are turned on when a cyclist is detected (Methorst et al., 2017). ...
Cycling is an environmentally friendly, economically beneficial and health-promoting means of transportation. Due to its great potential to cover short and medium distances, the use of the bicycle as a feeder mode to BRT is a priority on the public policy agenda. In addition to physical, level of service, and socioeconomic variables, safety perception is a factor playing a major role in the intention to use bicycles, and it may become more important for potential users than the infrastructure itself. Even though bicycle infrastructure and safety perception are linked, few studies have analyzed the effects that the bicycle infrastructure has on the safety perception. This paper studies how the characteristics of bike lanes influence safety perception and the intention to use bicycles as a feeder mode to BRT, using Bogotá’s Transmilenio as a case study. For this purpose, we used a hybrid discrete choice modeling approach to simultaneously estimate the attributes’ parameters explaining safety perception and the intention to use cycling as a feeder mode to BRT. Our findings showed that providing colored pavement, buffers with planters or buffers with safe hit posts increase both the safety perception and the potential demand of the bicycles as a feeder mode to BRT. Our modeling approach allowed us to identify combining promotion policies and measures of adaptation of existing infrastructure to increase bike lane coverage and encourage potential users to adopt the bicycle as a feeder mode to Transmilenio.
... More evidence is still needed, especially covering whole networks. Where aggregate exposure data exists, case-control methods can be used (e.g., [6,7,12]) however, at a national level such data is rarely available. This study uses a case-crossover design, allowing us to control for individual-level variation, unlike aggregate methods. ...
Objective:
This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak.
Methods:
The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case-control groups was used to compare characteristics of control and injury sites.
Results:
High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a 'safety in numbers' effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line ('bike box'), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads.
Conclusions:
Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.
... The knowledge about their usage patterns and risks has been accumulated through a large body of literature, followed by actual policy implementations for integrating bicycles into urban road spaces. Reynolds et al. [27] reviewed existing studies and identified risk factors determining injuries and crashes among bicyclists, especially in terms of the built environment: first, multi-lane roundabouts can significantly increase risk to bicyclists unless a separated cycle track is included in the design, as further examined in Reference [28], second, sidewalks and multi-use trails (i.e., sidewalks for both pedestrians and bicyclists) pose the highest risk, see Reference [29], and third, the presence of bicycle facilities (e.g., onroad bike routes, on-road marked bike lanes, and off-road bike paths) was associated with the lowest risk. Billot-Grasset et al. [30] emphasize that improved infrastructure design and maintenance is needed to avoid falls and collisions with obstacles. ...
As a mobility of future, the popularity of personal mobility vehicles (PMs) is rapidly increasing worldwide. However, this boom in the use of PMs has resulted in a substantial number of accidents involving not only PM users but also other road users including pedestrians, bicyclists, and motor vehicle drivers. This study aims to explore the potential risk factors for the occurrence of PM-related accidents and the resulting injury severity using the Traffic Accident Analysis System (TAAS) of South Korea between 2017 and 2019. We found that PM–pedestrian accidents tend to occur on roads with wider sidewalks and bike lanes, possibly because the pedestrian–PM conflict increases in this road condition. There is still ongoing debate on whether it is appropriate for PMs to share the sidewalk with pedestrians. Some countries, including Korea, prohibit the use of PMs on sidewalks; however, in reality, this regulation is not well-observed because using PMs on roadways involves higher crash risk with motor vehicles. This study suggests one potential solution to ensure safety of PM users: expansion of bike lane infrastructure having physically separated bike lanes and sidewalks/motorways in addition to the formation and strict enforcement of appropriate safety rules for PM users.
... Sanford et al., 2015;Hertach et al., 2018). One group furthermore identified on-road tram rails as a statistically significant risk factor for the incidence of bicycling accidents (Vandenbulcke et al., 2014). Two typical mechanisms of accidents related to tramways have been suggested in pertinent literature: Wedging of bicycle wheels in (on-road) tram rails and skidding of bicycle wheels on the tram rails (Deunk et al., 2014;Teschke et al., 2016;Hertach et al., 2018). ...
Bicycle traffic and the number of accidents have been increasing in Switzerland in recent years. However, little is known about specific types of bicycling accidents – such as tramline-associated bicycling accidents (TABA) – that are potentially avoidable.
This retrospective single center study of emergency department (ED) consultations analyzed TABA in the city of Bern, Switzerland. We analyzed the medical records of adult patients who presented to the ED after any bicycle accident. Patient and consultation characteristics were extracted. Incidence, important characteristics and ED resource consumption of TABA were compared with bicycle accidents that did not involve tramlines (BA). Furthermore, injury patterns and predictors of resource consumption were determined in TABA.
We included a total of 298 TABA and 2351 BA over the 5.6-year study period. TABA accounted for 11.2 % of all bicycling accidents. Descriptive analysis revealed a significant increase between 2013 (lowest) and 2016 (highest) of 33.0 % in the total number of BA and 132 % in the total number of TABA. Compared to BA, TABA patients were significantly older, more often female, had a less urgent triage, and less often needed resuscitation bay treatment, hospitalization or ICU-admission (all p < 0.05). In multivariable analysis, TABA were associated with greater needs for ED resources. Most TABA injuries were to limbs (70.5 %) or the head (53.0 %), including intracranial hemorrhage (2.3 %). The radiological work-up consumed most of the resources (37.5 %), followed by physicians’ work (25.8 %). Statistically significant predictors of ED resource consumption in TABA were age, triage, resuscitation bay treatment, injuries to head/clavicle, and intoxication. One out of ten patients was intoxicated.
Although TABA is associated with less severe trauma than BA, it bears the risk of significant morbidity and high ED resource needs. Intoxications contribute to this problem. Our findings underline the need for preventive measures to reduce TABA in the future.
... To assess how characteristics of the route environment affect injury risk for each individual road user, we need to control for exposure (Vandenbulcke, Thomas, and Int Panis 2014;Vanparijs et al. 2015). Epidemiological methods offer ways of doing this. ...
This study of cycling injury risk in London examines impacts of road characteristics and environment, including different types of cycling infrastructure. It controlled for exposure by using a case-crossover method alongside an algorithm developed by Transport for London to predict cyclist routes. When compared to no infrastructure, this study found that protected cycle infrastructure reduced odds of injury by 40-65% in the morning commute, whereas advisory lanes increased injury odds by 34%. Junctions were found to increase injury odds threefold; higher pedestrian density also increased injury odds. This study supports growing evidence of a ‘safety in numbers’ effect.
... As seen for pedestrians, Martinez-Ruiz et al. (2015) analyzed the cyclist risks in Spain according to gender and age parameters. Reynolds et al. (2009), Vandenbulcke et al. (2014 and Robartes and Chen (2017) determined the cycling risk by taking into account factors like infrastructure, environment, and traffic. With the risk information, acting on some of those factors could be an effective way for injury reduction. ...
In 2016, road fatalities reached 1.35 million in the world according to the World Health Organization. 26% of these fatalities were pedestrians and cyclists. Nowadays, more and more cars are equipped with an emergency system (called FCW – Forward Collision Warning) that can detect pedestrians and cyclists in order to warn drivers of a hazardous situation. These systems can also help in collision avoidance either by assisting driver during braking or by activating an Automatic Emergency Braking (AEB). This thesis is focused on Pedestrian and Cyclist AEB and FCW assessment and has three main objectives.First, an analysis on more than 3700 accident case reconstructions (2200 cyclist cases and 1500 pedestrian cases) from two databases, one French and one German has been performed. Accident configurations have been extracted and classified into different scenarios. A simulation software has been implemented in order to replay the accident kinematics with the integration of an AEB by varying their system characteristics. This allows the identification of optimum characteristics for a pedestrian AEB and cyclist AEB in terms of road user detection. It also allows identifying FCW trigger time and the duration of an emergency braking.Secondly, based on an experimental campaign using a driving simulator, the driver’s reactions to a FCW signal have been analyzed on different accident configurations: pedestrian/cyclist cases, with/without FCW and with different FCW triggers. Two hundred volunteers participated in this experiment. The results concern the gaze analysis, the driver’s response to the FCW signal, the time reaction to trigger a braking and the different behavior depending on the driving configurations.The third objective concerns the benefits assessment of a FCW. Based on the results of the driving simulator experiment and the kinematic reconstructions of the accidents, benefits of a FCW are estimated in terms of potential avoided or mitigated accidents.Finally, some perspectives of this work are proposed.
... Despite several governmental and municipal programs aimed at increasing the popularity of cycling (e.g., Encouraging cycling in central London|Space Syntax, 2000; ''Nationaler Radverkehrsplan 2020'', Reichel, 2015), the proportion of trips traveled by bike as compared to other modes of transportation remains limited in most countries. Consequently, there is a rich body of research geared towards identifying individual and infrastructural factors that contribute to the risk of cycling crashes (e.g., Aldred, Goodman, Gulliver, & Woodcock, 2018;Prati, Puchades, Angelis, Fraboni, & Pietrantoni, 2018;Vandenbulcke, Thomas, & Int Panis, 2014). And indeed, the risk associated with typical urban traffic conditions has been found to be one of the major deterrents to cycling (Aldred, 2016;Parkin, Wardman, & Page, 2007;Winters, Davidson, Kao, & Teschke, 2011). ...
In this research, we investigate how gaze behavior during urban cycling is affected by subjective risk perception and the local spatial configuration. For this purpose, participants cycled at five defined test locations while wearing a mobile eye tracking system. Next to the fixation duration, we extracted sight vector lengths (i.e., the distance between the current body location and the gaze location) and gaze angles (i.e., the degree to which the gaze deviates from the current travel direction). Concerning risk perception, participants provided a hazard estimate for each test location, as well as information about specific areas they experienced as dangerous. This information was aggregated into a local hazard level across all participants. Finally, the vista space (i.e., the space visible from the current position during a fixation) was quantified via isovist analysis. The results imply that spatially open locations, extending about equally into all directions, increase the level of perceived risk. Depending on the local spatial properties, participants also gaze into different directions, rather than further ahead and into their travel direction. Higher cycling experience and greater familiarity with a location enabled a more foresighted and focused gaze behavior. Our findings underline the importance to provide cyclists not only with enough space and with unobstructed views, but also to limit the space they are required to distribute their attention to.
... Gundogdu, 2010;Truong and Somenahalli, 2011;Qin et al., 2013); conducting spatial analysis to identify high risk sites (e.g. Aguero-Valverde and Jovanis, 2006;Erdogan, 2009;Vandenbulcke et al., 2014;Deshpande et al., 2011;Ivan et al., 2015;Rahman et al., 2017;Vandenbulcke et al., 2017;Harirforoush et al., 2019;Yao et al., 2018;Le et al., 2019); and using it to apply spatio-temporal analysis (Prasannakumar et al., 2011;Ivan and Haidu, 2012;Achu et al., 2019;Kaygisiz and Sümer, 2019;Pleerux, 2020). ...
Objective
Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy and healthcare. There is little systematic understanding of the geography of RTCs and the spatial correlations of RTCs in the Middle-East region, particularly in Oman where RTCs are the leading cause of disability-adjusted life years lost. The overarching goal of this paper is to evaluate the spatial and temporal dimensions, identifying the high risk areas or hot-zones where RTCs are more frequent using the geocoded data from the Muscat governorate. Data: This study is based on data drawn from the Royal Oman Police (ROP) sample iMAAP database and the National Road Traffic Crash (NRTC) database which is managed by the ROP and made available for research use by The Research Council of the Sultanate of Oman. The data covered the period from 1st January 2010 to 2nd November 2014. Only RTCs occurred in Muscat Governorate were included in the study. The study is based on 12,438 registered incidents, however, due to disconnections found on road network, RTCs occurred on disconnected parts were removed and the final analysis considered only 9,357 incidents.
Methods
The analysis considered an adjacency network analysis integrating GIS and RTC data using robust estimation techniques including: Kernel Density Estimation (KDE) of both 1-D and 2-D space dimensions, Network-based Nearest Neighbour Distance (Net-NND), Network-based K-Function, Random Forest Algorithm (RF) and spatiotemporal Hot-zone analysis. Findings: The analysis highlight evidence of spatial clustering and recurrence of RTC hot-zones on long roads demarcated by intersections and roundabouts in Muscat. The findings confirm that road intersections elevate the risk of RTCs than other effects attributed to road geometry features. The results from GIS application of NRTC data are validated using the sample data generated by iMAAP database.
Conclusion
The findings of this study provide statistical evidence and confirm the research hypothesis that that road intersections (roundabouts, crosses and bridges) represent higher risk of causing RTCs than other road geometric features. The results also demonstrate systematic quantitative evidence of spatio-temporal patterns associated with the crash risk over different locations on road network in Muscat. More importantly, the findings clearly pinpoint the importance and influence of the road and traffic related feature in road crash spatial analysis.
... accidents). The most recent examples of CC method's application published in the road safety literature include US studies on 1) accident risk levels related to traffic oscillations in congestion (Zheng et al., 2010), and 2) different "policy-sensitive" factors on motorcycle accidents (Wali et al., 2018), 3) a South Korean study on the safety effects of highway terrain type on major rural roads (Choi et al., 2011), 4) a French study on the relationship between wearing a bicycle helmet and risking different types of injury (Amoros et al., 2012), 5) a spatial study that attempts to predict cycling accident risk in Brussels (Vandenbulcke et al., 2018), and 6) a UK study exploring the impact of traffic volumes and road characteristics on cycling injury risk (Aldred et al., 2018). ...
This study estimates the effects of lane and shoulder widths on occurrence of head-on and single-vehicle accidents on rural two-lane undivided roads in Norway while considering the differences between winter and non-winter accidents and their severity levels. A matched case-control method was applied to calculate the odds ratios for lane and shoulder width categories, while controlling for the effects of AADT and adjusting for the effects of region, speed limit, segment length, share of long vehicles in AADT and horizontal alignment. The study used a sample of 71,999 roadway segments identified in GIS and 1886 related accidents recorded by the police in five-year period. The results suggest that it is relevant to consider winter and non-winter accidents as well as severe and slight accidents separately when studying the effects of lane and shoulder widths on the occurrence of head-on and single-vehicle accidents. When examining lane and shoulder widths for all related accidents, the lane widths 1.50–2.50 m and shoulder widths 0.50−0.75 m were relatively safer than other categories on Norwegian two-lane rural undivided roads.
... It could be attributed to the conflicts between motor vehicles, bicycles and pedestrian (Narayanamoorthy et al., 2013). For the effect of traffic management and control attributes, increase in bicycle crash frequency was found correlated to the increase in the density of intersection (Siddiqui et al., 2012;Wei and Lovegrove, 2013;Pulugurtha and Thakur, 2015), traffic signal (Guo et al., 2018c;de Geus et al., 2012;Chen, 2015), presence of cycle lanes (Reynolds et al., 2009;Hamann and Peek-Asa, 2013;Wei and Lovegrove, 2013;Chen et al., 2016), and presence of onstreet parking (Wei and Lovegrove, 2013;Vandenbulcke et al., 2014). Furthermore, environmental factors including landscape and weather conditions can also affect the level of service and safety of bicyclists (Vanparijs et al., 2015;Xing et al., 2019;Zhai et al., 2019;Fournier et al., 2017;El-Assi et al., 2015). ...
Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012−2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.
Contraflow cycling on one-way streets is a low cost intervention that research shows can improve the cycling experience and increase participation. Evidence from several studies suggest that cyclists on contraflows have a lower crash risk. However, implementing contraflow cycling is often controversial, including in the United Kingdom (UK). In this paper we examine whether contraflow cycling on one-way streets alters crash or casualty rates for pedal cyclists.
Focusing on inner London boroughs between 1998 and 2019, we identified 508 road segments where contraflow cycling was introduced on one-way streets. We identified road traffic crashes occurring within 10 m of these segments and labelled them as pre-contraflow, contraflow or contraflow removed crashes. We calculated rates using the number of crashes or casualties divided by the time exposed and generated 95 % confidence intervals using bootstrap resampling. We adjusted the rates for changes in cordon cycling volume and injury severity reporting.
There were 1498 crashes involving pedal cyclists: 788 pre-contraflow, 703 contraflow and 7 following contraflow removal. There was no change in adjusted overall pedal cyclist crash or casualty rates when contraflow cycling was introduced. Proximity to a junction doubled the crash rate. The crash rate when pedal cyclists were travelling contraflow was the same as those travelling with flow.
We have found no evidence that introducing contraflow cycling increases the crash or casualty rate for pedal cyclists. It is possible that such rates may indeed fall when contraflow cycling is introduced if more accurate spatio-temporal cycling volume data was available. We recommend all one-way streets are evaluated for contraflow cycling but encourage judicious junction design and recommend UK legislative change for mandatory-two-way cycling on one-way streets unless exceptional circumstances exist.
This paper presents a preliminary study for the assessment of tram safety by managing Advanced Driver Assistance Systems depending on the Grades of Automation. The Grades/Levels of Automation in automotive, aeronautics, maritime and railway systems are presented and compared with each other. Then, according to especially the implication level of a haptic system in each tram driving task, Grades of Automation for trams are proposed. In addition to the haptic system, a visual one that uses a Head-Up Display is defined. These systems are designed to help the tram driver cope with potential hazards by having a defensive driving. Therefore, the proposed Grades of Automation and the driver assistance systems are used in order to propose an experimental method to explore how this automation can affect the tram driver performance and the Human-Machine System safety.
Introduction:
Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks.
Method:
The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis.
Results:
The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified.
Conclusion:
Based on these results, a series of specific, suggested policy changes are presented for implementation consideration.
Practical applications:
There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.
The identification of accident black spots is of great significance for the prevention of traffic accidents. Commonly used accident black spot identification methods divide road sections for the analysis of accident data, the direct result of which is the splitting of accident black spots, which affects the results. This paper is based on three years of traffic accident data from the Beijing-Harbin Expressway, including the time and location of traffic accidents, form of the accident fatalities, severe injuries, slight injuries, and property damage only (PDO). To avoid road division effects, an identification method based on the accident spacing distribution is established by using quality control theory. The results show that the average number of accidents per kilometer by the method proposed in this paper is 42, which is much higher than 10, identified by other identification methods. The method proposed in this paper improves the accuracy of the identification results. This method avoids the problem of road segmentation found in other common methods and can more accurately reflect the spatial distribution of traffic accidents. Thus making the identification of accidents more scientific and accurate.
Vulnerable road users (VRUs) constitute an increasing proportion of the annual road fatalities across Europe. One of the crash types involved in these fatalities are blind spot crashes between trucks and bicyclists. Despite the presence of mandatory blind spot mirrors, truck drivers are often reported to have overlooked the presence of a bicyclist. This raises the question if and when truck drivers check their blind spot mirrors for the presence of bicyclists, and which factors contribute to such glance behavior. The current study presents the results of an analysis of naturalistic glance behavior by 39 truck drivers in 1,903 right-turning maneuvers at urban intersections, where in each maneuver there was a chance of crossing the path of a bicyclist. The descriptive analysis revealed that most often truck drivers did not cast a glance upon their blind spot mirrors as recommended by the examination guidelines. Furthermore, a choice model was developed with the main factors that have an impact on glance behavior. Drivers were more likely to glance with a priority regulation that allowed conflicts, with lower speed limits, with zebra crossings, without cyclist facilities, without a lead vehicle making the same maneuver, in presence of VRUs, without adverse sight conditions, in lower age groups, without certain non-driving related activities, when driving a truck with more direct vision on VRUs, and without a camera providing a view on the blind spot, and with less time between a standstill and starting the maneuver. Three factors did not significantly improve the choice model and were therefore left out, despite showing significant effects in bivariate tests: intersection layout (e.g., three vs. four legs), presence of advanced stopping lanes, and visual obstruction. Implications of the choice model are discussed for driver education (in terms of timely glances, reducing inattention, and hazard anticipation) and vehicle design (in terms of direct vision).
Transportation and technological development have for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, also has a negative impact on urban areas and their surroundings. Many transportation and technological solutions lead, for example, to increased levels of pollution, noise, excessive energy use, as well as to traffic accidents in cities. So, it is important to safe urban development and sustainability in all city aspects as well as in the area of road transport safety. Due to the long-term policy of sustainable transport development, cycling is promoted, which contributes to the increase in the number of this group of users of the transport network in road traffic for short-distance transport. On the one hand, cycling has a positive effect on bicyclists’ health and environmental conditions, however, a big problem is an increase in the number of serious injuries and fatalities among bicyclists involved in road incidents with motor vehicles. This study aims to identify factors that influence the occurrence and severity of bicyclist injury in bicyclist-vehicle crashes. It has been observed that the factors increasing the risk of serious injuries and deaths of bicyclists are: vehicle driver gender and age, driving under the influence of alcohol, exceeding the speed limit by the vehicle driver, bicyclist age, cycling under the influence of alcohol, speed of the bicyclist before the incident, vehicle type (truck), incident place (road), time of the day, incident type. The obtained results can be used for activities aimed at improving the bicyclists’ safety level in road traffic in the area of analysis.
Cyclists need to constantly scan their surroundings for potential hazards. This is particularly true relevant for intersections with cars approaching from different directions. Our knowledge about how cyclists allocate their visual attention towards certain directions (or not) is so far limited. We designed a virtual intersection that participants crossed in several variations on a virtual bike while wearing a head mounted display. We hypothesized that the visual accessibility of intersection branches affect the attention towards the respective direction as well as the attribution of risk are perceived as less dangerous than areas of medium visual accessibility. We found that subjective risk perception differed significantly between the street branches, independent of the travelling direction, as well as depending on the way the intersection had to be crossed (e.g. a sharp turn increased the hazard estimate in general). Contrasting our expectations, we found no evidence that the spatial position of an intersection branch in relation to the traveling direction affected the participant's hazard estimate. In other words, intersection branches located more to the right or left were not rated more dangerous, although it should be more difficult to spot cars approaching from these directions. We discuss our experiences with VR studies as a method for studying cycling safety and outline a subsequent study addressing the identified issues.
Cycling is encouraged in countries around the world as an economical, energy-efficient, and sustainable mode of transportation. Simulation is an important approach to analyzing the safety of cycling by identifying the effects of different factors. To ensure the success of a simulation study, it is essential to know the factors that have significant effects on bicycle safety. Although many studies have focused on analyzing bicycle safety, they lack bicycle exposure data, which could introduce biases for the identified factors. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments considering the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data that includes bicycle exposure information was used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections.
The developed model was compared with three counterparts: a model using the un-adjusted STRAVA data, a model using the STRAVA data with field observation data adjustments only, and a model using the STRAVA data with adjusted population. The results revealed that the STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling.
The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) that are associated with bicycle safety at intersections. It is recommended that the effects of these identified factors be explored in simulation studies. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data is a reliable source for exploring bicycle safety after appropriate adjustments.
Geo-visualization is a subfield of Geoinformatics that draws attention both on visualization research, spatial algorithm research in data mining and machine learning. The chapter investigates how visualization can be successfully applied to all phases of problem-solving in geographical analysis, from initial hypothesis development through knowledge discovery, analysis, presentation and evaluation. This chapter also supports the application of methods of geovisualization to the major problems in GIScience. Several graphical representations (information spaces) are used to describe the general structure and clustering of the data and to gain insight into the relationships between the various variables.
As roundabouts become increasingly popular, and as many communities promote bicycle use, the safety of roundabouts for people bicycling is of major concern. Although converting an intersection to a roundabout may reduce crashes overall, some research from northern Europe suggests that roundabouts may actually increase the frequency of bicycle crashes. We perform a systematic literature review on this topic, reviewing 49 different resources with empirical findings (most from Europe, some from Australia/New Zealand, few from the US). Many studies analyse (limited) bicycle crash data or observe driver/cyclist behaviours and interactions, while a few survey cyclists’ safety perceptions. Consistent with design guidance, bicycle safety performance is worse for higher-speed, multilane roundabouts and when on-roadway bike lanes are provided. Crash data and observations suggest that when cyclists “take the lane” and operate as vehicles – as is allowed or even recommended in some current design guidelines – this leads to conflicts and crashes between circulating cyclists and entering drivers who may have “looked but failed to see” (and thus failed to yield to) the cyclist. Providing separated cycle paths around the roundabout seems to be a lower-risk and more comfortable design solution, although care must be taken to encourage appropriate yielding at crossings. Future research should investigate more design features, socio-demographic characteristics, cyclist safety perceptions, and impacts outside of Europe. Studies should continue to explore ways to overcome limited bicycle crash and exposure data and to utilise naturalistic methods, driving simulators, and stated choice experiments.
Cycling helps reduce traffic congestion, environmental pollution and promote a healthy lifestyle for the general public. However, it could also expose cyclists to dangerous environments, resulting in severe consequences and even death. Transport authorities are seeing growing accidents in city regions with increasing cycling population, requiring the development of new risk informed cycling safety policies. This paper aims to develop a new conceptual risk analysis approach based on a Bayesian network (BN) technique to enable the analysis and prediction of the severity of cycling accidents. To identify the risk factors influencing accident severity, 2,269 cycling accident reports from the UK city region were manually collected, where primary data was extracted and analysed. An advanced data training method (i.e. Tree Augmented Naïve Bayes (TAN)) for BN development was applied to investigate their correlation and their individual and combined contributions to cycling accident severity. As a result, the risk factors influencing accident severity are prioritised in terms of their risk contribution. The risk levels of accident severity can be predicted in dynamic situations based on the data from simulated and/or real cycling environments. The findings can provide useful insights for making rational cycling safety policies in proportion to different risk levels.
Severe and even fatal accidents between cyclists and motor vehicles commonly occur at intersections. Many of these accidents occur with right-turning vehicles, with drivers not observing an adjacent cyclist. Few structured investigations exist regarding the interaction between cyclist and motor vehicle, and factors in need of study are how infrastructure and vehicle properties affect human decision-making and cycling behaviour. Therefore, a bicycle simulator study was performed, where vehicle type, presence of lane markings and lane width were systematically varied in a scenario with a cyclist approaching a vehicle from behind, at a signalized city intersection. 33 participants cycled through 8 intersection variants each. Data on cycling trajectories, stopping points and speed was coupled with survey data from the participants, and semantically categorized verbal responses to questions regarding strategy for choice of stopping point. Results show that all three factors (vehicle type, lane markings and available vehicle-adjacent space) significantly affects cyclists’ behaviour and conscious strategies. Participants were more cautious in the presence of a truck than a car, reflected in choice of position when cycling and stopping, and in explicit verbalisations regarding perilous aspects of the situation and their conscious and strategic choice of positioning. Available lateral space also affected stop positions and feeling of safety (expressed verbally). Presence of bicycle lane markings made the cyclists inclined to continue into the space to the right of the vehicle. This was revealed by their positioning and speed, and also apparent in the verbal expressions, especially the positive remarks on the situation and conditions. However, the perceived comfort with lane markings present was actually lower than when they were missing. Cyclist type (slow, moderate, or fast) matters with the self-reported faster cyclists being more prone to stop to the right than the slower one.
Zonal characteristics (e.g. built environment, network configuration, socio-demographics, and land use) have been shown to affect biking attractiveness and safety. However, previously developed bikeability indices do not account for cyclist-vehicle crash risk. This study aims to develop a comprehensive zone-based index to represent both biking attractiveness and cyclist crash risk. The developed Bike Composite Index (BCI) consists of two sub-indices representing bike attractiveness and bike safety, which are estimated using Bike Kilometers Travelled (BKT) and cyclist-vehicle crash data from 134 traffic analysis zones (TAZ) in the City of Vancouver, Canada. The Bike Attractiveness Index is calculated from five factors: bike network density, centrality, and weighted slope as well as land use mix and recreational density. The Bike Safety Index is calculated from bike network coverage, continuity, and complexity as well as signal density and recreational density. The correlation between the Bike Attractiveness Index and the Bike Safety Index in Vancouver is low (r = 0.11), supporting the need to account for both biking attractiveness and safety in the composite index.
We examined the public health consequences of unsafe and inconvenient walking and bicycling conditions in American cities to suggest improvements based on successful policies in The Netherlands and Germany.
Secondary data from national travel and crash surveys were used to compute fatality trends from 1975 to 2001 and fatality and injury rates for pedestrians and cyclists in The Netherlands, Germany, and the United States in 2000.
American pedestrians and cyclists were much more likely to be killed or injured than were Dutch and German pedestrians and cyclists, both on a per-trip and on a per-kilometer basis.
A wide range of measures are available to improve the safety of walking and cycling in American cities, both to reduce fatalities and injuries and to encourage walking and cycling.
Background: Travel surveys in Europe and the U.S. show large differences in the proportion of walking and cycling trips without considering implications for physical activity. Purpose: This study estimates differences between Germany and the U.S. over time in population levels of daily walking and cycling at different health-enhancing thresholds across sociodemographic groups. Methods: Uniquely comparable national travel surveys for the U.S. (NHTS 2001 and 2009) and Germany (MiD 2002 and 2008) were used to calculate the number, duration, and distance of active trips per capita. The population-weighted person and trip files for each survey were merged to calculate population levels of any walking/cycling, walking/cycling 30 minutes/day, and achieving 30 minutes in bouts of at least 10 minutes. Logistic regression models controlled for the influence of socioeconomic variables. Data were analyzed in 2010. Results: Between 2001/2002 and 2008/2009, the proportion of "any walking" was stable in the U.S. (18.5%) but increased in Germany from 36.5% to 42.3%. The proportion of "any cycling" in the U.S. remained at 1.8% but increased in Germany from 12.1% to 14.1%. In 2008/2009, the proportion of "30 minutes of walking and cycling" in Germany was 21.2% and 7.8%, respectively, compared to 7.7% and 1.0% in the U.S. There is much less variation in active travel among socioeconomic groups in Germany than in the U.S. German women, children, and seniors walk and cycle much more than their counterparts in the U.S. Conclusions: The high prevalence of active travel in Germany shows that daily walking and cycling can help a large proportion of the population to meet recommended physical activity levels. (Am J Prev Med 2011;41(3):241-250) © 2011 American Journal of Preventive Medicine
Introduction: Bayesian modeling in the 21st centuryDefinition of statistical modelsBayes theoremModel-based Bayesian inferenceInference using conjugate prior distributionsNonconjugate analysisProblems
Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes – the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.
Predictive vegetation modeling (PVM), is defined as predicting the distribution of vegetation across a landscape based on the relationship between the spatial distribution of vegetation and environmental variables. PVM requires digital maps of the environmental variables, as well as spatial information on the vegetation attribute of interest (e.g., species, type, abundance), usually from a sample of locations. Often these predictive models are developed using traditional statistical methods and are based on the implicit assumption that the distribution of vegetation is random and, therefore, each observation is independent. This approach violates one of the basic tenets of geography, the direct relationship between distance and likeness, as well as of ecological theory, that elements of an ecosystem close to one another are more likely to be influenced by the same generating process and will therefore be similar. Some of the spatial structure can be explained by the predictor variables used in the model. Environmental variables such as precipitation, temperature and elevation exhibit spatial dependence, some of which is responsible for spatial clustering in vegetation distribution, but remaining spatial dependence can result from either unmeasured environmental variables or biotic processes that cause spatial clustering. Spatial dependence in biogeographical data has been recently identified as an important area of future PVM research, and many studies have begun to explore ways to incorporate spatial dependence in predictive models. Here we review the different approaches to incorporating spatial dependence into predictive vegetation models focusing on four statistical methods: autoregressive models, geostatistics, geographically weighted regression, and parameter estimation models. Autoregressive models may be more capable of describing the fine-scaled spatial dependence that results from local biotic factors, such as disturbance, competition, or dispersal, while geostatistical methods may be more suitable for modeling broad-scale spatial dependence. The other methods focus on global and local parameter estimation in the presence of spatially structured or nonstationary data. While this review focuses on incorporating spatial dependence into statistical models for predictive purposes, explicitly including spatial dependence in models can also aid in clarifying the effect of different explanatory variables, thereby improving inferences.
This paper focuses on the integration of models, especially potential models, in a geographical information system (GIS). This exercise was prompted by the inability of common geographical information systems to deal adequately with the problem of accessibility. Attention is devoted to the technical aspects of integration as well as to the use of GTS—based potential modelling in Dutch physical planning practice.
A hands-on introduction to the principles of Bayesian modeling using WinBUGS. Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference. Generalized linear models. Bayesian hierarchical models. Predictive distribution and model checking. Bayesian model and variable evaluation. Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.
Mapping transforms spatial data into a visual form, enhancing the ability of users to observe, conceptualize, validate, and communicate information. Research efforts in the visualization of traffic safety data, which are usually stored in large and complex databases, are quite limited at this time. This paper shows how hierarchical Bayes models, which are being vigorously researched for use in disease mapping, can also be used to build model-based risk maps for area-based traffic crashes. County-level vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses hierarchical models to develop network-based risk maps is also discussed.
This chapter is concerned with a more detailed explanation of some of the methods that are provided for working with the spatial classes described in Chap. 2. We first consider the question of the spatial support of observations, going on to cover the handling and combination of features using in particular the rgeos package. Next we consider map overlay, also known as spatial join operations, including aggregation, extract operations in the raster package, and spatial sampling.
Spatial and spatio-temporal data are everywhere. Besides those we collect ourselves (‘is it raining?’), they confront us on television, in newspapers, on route planners, on computer screens, on mobile devices, and on plain paper maps. Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is however not easy. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. These questions refer to hypothetical processes that generate the observed data. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us.
Although much work has been devoted to developing crash severity models to predict the probabilities of crashes for different severity levels, few studies have considered the underreporting issue in the modeling process. Inferences about a population of interest are biased if crash data are treated as a random sample from the population without consideration of the different unreported rates for each crash severity level. The primary objective of this study was to examine the effects of underreporting for three commonly used traffic crash severity models: multinomial logit (MNL), ordered probit (OP), and mixed logit (ML) models. The objective was accomplished with a Monte Carlo approach that used simulated and observed crash data. The results showed that, to minimize the bias and reduce the variability of a model, fatal crashes should be set as the base-line severity for the MNL and ML models, while for the OP models, the rank for the crash severity should be set from fatal to property damage only in a descending order. None of the three models was immune to this underreporting issue. When full or partial information about the unreported rates for each severity level was known, treatment of the crash data as outcome-based samples in model estimation (through the weighted exogenous sample maximum likelihood estimator) dramatically improved the estimation for all three models as compared with the results produced from the maximum likelihood estimator.
Motivated by the current debate on possible raised incidence of certain types of cancers near nuclear installations, this paper develops a methodology for fitting a class of inhomogeneous Poisson point process models to data consisting of the locations of all occurrences of some phenomenon of interest within a designated planar region. The model is based on a multiplicative decomposition of the intensity function, with separate terms to describe natural spatial variation in intensity and possible raised incidence around a prespecified point. A nonparametric kernel smoothing approach, based on data from a related phenomenon, is used to describe natural spatial variation, while a parametric maximum likelihood approach is used to describe raised incidence near the prespecified point. The methodology is applied to data on the spatial distribution of cancers of the larynx and of the lung in the Chorley-Ribble area of Lancashire, England.
This article evaluates the factors associated with the crash risk of adult bicyclists. A logistic regression analysis was used to determine and quantify risk factors, controlling simultaneously for a number of rider characteristics and bicycle use patterns. The analysis was based on data from a national survey of over 3,000 adult bicyclists, age 18 and older. The survey gathered information on the characteristics and use patterns of the bicyclists, and whether they had crashed or fallen from their bicycles during the preceding year. The results of the analysis show that the bicycle crash risk is systematically related to a rider's age, riding distances, riding surface, bicycle type, and geographical region of residence.
Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Moran's I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.
Most developed countries nowadays face environmental, health and mobility problems as a consequence of widespread car use. Policies are now being reappraised in favour of more sustainable modes of transport. In particular, bicycle use holds the potential to provide a ‘green’ and healthy alternative to car commuting. There are however still important barriers that discourage people cycling… This thesis aims at identifying some of the main factors that influence cycle commuting and cycling accidents. Identifying such factors would in turn provide greater support to enable policy makers developing supportive environmental conditions for cycling. In the first part of this thesis, we examine which factors influence the spatial variation of bicycle use for commuting to work at the level of the municipalities in Belgium. Special attention is paid to bicycle-specific factors and spatial econometric methods are used to account for the presence of spatial effects in the data. The second part of this thesis examines which factors are associated with cycling accidents in Brussels. Spatial point pattern methods extended to networks are used to compare the ‘locational tendencies’ of cycling accidents officially registered by the police with those that are unregistered. An innovative case-control approach, based on a rigorous sampling design of controls and an exhaustive data collection of spatial factors, is also proposed to allow modelling the risk of cycling accident along the Brussels’ road network. This thesis not only provides sound recommendations helping planners and policy makers to encourage bicycle use, but it also offers new research directions for pinpointing locations where accidents are more likely to occur.
Background:
For an accurate estimation of health benefits and hazards of utilitarian cycling, a prospective collection of bicycle usage data (exposure) is fundamental. Individual and environmental correlates are necessary to guide health promotion and traffic safety issues. Firstly, this study aims to report on utilitarian bicycle usage in Belgium, using a prospective data collection in regular adult commuter cyclists. Secondly, the association is explored between the individual variation in bicycle usage and individual and environmental correlates.
Methods:
1187 regular adult cyclists filled out travel diaries prospectively. Multivariate linear regression with Stepwise selection (SMLR) models studied the association between exposure and individual and environmental correlates.
Results:
Higher age and availability of cycle paths have a positive association with bicycle usage to work. Women cycle significant less compared with men, and so do cyclists with 'poor' or 'average' health. Living in an urban crown (opposed to city center) and living in Flanders (opposed to Brussels or Wallonia) is associated with significantly more cycling.
Conclusions:
Utilitarian cycling is related to regional differences, level of urbanization of the place of residence, availability of bicycle paths, and gender. These findings are useful in estimating health benefits and hazards of utilitarian cycling among regular Belgian cyclists.
A textbook providing explanations of many modern spatial data analysis methods. The text and illustrative case studies are complemented by a custom statistical analysis package and case-study datasets. The software (INFO-MAP) will run on IBM-PC compatibles running DOS. The contents are organised into the following categories: spatial data analysis including the use of computers; the analysis of point patterns; the analysis of spatially continuous data; the analysis of area data; the analysis of spatial interaction data. The authors examine formal statistical modelling and informal, exploratory techniques, relating them to the analysis of real data; explain the underlying statistical methodology and provide a structure for further learning and exploration through reading lists and computer exercises using the software and data provided. -M.Dean
Cycling has to be a safe activity, and perceived as such, if bicycle trips by all populations are to increase and the public health benefits are to be realized. A key characteristic of developed countries with a high cycling mode share is their provision of cycle tracks - separated bikeways along city streets - on major routes. This literature review therefore sought to examine studies of cycle tracks from different countries in order elucidate the safety of these facilities relative to cycling in the street and to point to areas where further research is needed. The review indicates that one-way cycle tracks are generally safer at intersections than two-way and that, when effective intersection treatments are employed, constructing cycle tracks on busy streets reduces collisions and injuries. The evidence also suggests that, when controlling for exposure and including all collision types, building one-way cycle tracks reduces injury severity even when such intersection treatments are not employed. However, the extent of this effect has not been well examined, as very few studies both look at severity and control for exposure. Future studies of the safety of cycle tracks and associated intersection treatments should focus foremost on examining injury severity, while controlling for exposure. In the U.S., where the obesity epidemic and its health consequences and costs are well documented, the benefits of increased cycling should be a focus of research and policy development in order to provide the infrastructure needed to attract people to cycling while minimizing injuries.
This paper analyses the findings in relation to safety of research on the experience of cycle facilities introduced in parts of the Greater Nottingham area since the early 1980s. As well as drawing on the monitoring studies by JMP Consultants for the TRL it discusses in particular the findings of research carried out at Nottingham University on the attitudes of cyclists and non-cyclists. It shows how, on balance, special facilities are valued as enhancing cyclists′ safety, despite a number of detailed criticisms. It then discusses these findings in relation to general traffic planning as well as cycle policy.
Concern over crashes involving bicycles and motor vehicles is largely due to the severity of injuries. This research examines the impacts of physical and environmental factors on the severity of injury to bicyclists. North Carolina Highway Safety Information System (HSIS) crash and inventory data for statecontrolled two-lane undivided roadways are analyzed. The injury severity distribution, measured on the KABCO scale, is as follows: no injury 1.8%; complaint of pain 24.4%; non-incapacitating injury 42.5%; incapacitating injury 25.5%; and fatal injury 5.9%. The total number of involvements in this dataset was 1025 with a majority of the involvements occurring outside urbanized areas (80.5%). Using the ordered probit model, the effect of a set of roadway, environmental, and crash variables on injury severity is explored. Variables that significantly increase injury severity include straight grades, curved grades, darkness, and fog. Higher average annual daily traffic was the only variab...
BACKGROUND Introduction Data Sets Bayesian Inference and Modeling Likelihood Models Prior Distributions Posterior Distributions Predictive Distributions Bayesian Hierarchical Modeling Hierarchical Models Posterior Inference Exercises Computational Issues Posterior Sampling Markov Chain Monte Carlo Methods Metropolis and Metropolis-Hastings Algorithms Gibbs Sampling Perfect Sampling Posterior and Likelihood Approximations Exercises Residuals and Goodness-of-Fit Model Goodness-of-Fit Measures General Residuals Bayesian Residuals Predictive Residuals and the Bootstrap Interpretation of Residuals in a Bayesian Setting Exceedence Probabilities Exercises THEMES Disease Map Reconstruction and Relative Risk Estimation An Introduction to Case Event and Count Likelihoods Specification of the Predictor in Case Event and Count Models Simple Case and Count Data Models with Uncorrelated Random Effects Correlated Heterogeneity Models Convolution Models Model Comparison and Goodness-of-Fit Diagnostics Alternative Risk Models Edge Effects Exercises Disease Cluster Detection Cluster Definitions Cluster Detection using Residuals Cluster Detection using Posterior Measures Cluster Models Edge Detection and Wombling Ecological Analysis General Case of Regression Biases and Misclassification Error Putative Hazard Models Multiple Scale Analysis Modifiable Areal Unit Problem (MAUP) Misaligned Data Problem (MIDP) Multivariate Disease Analysis Notation for Multivariate Analysis Two Diseases Multiple Diseases Spatial Survival and Longitudinal Analyses General Issues Spatial Survival Analysis Spatial Longitudinal Analysis Extensions to Repeated Events Spatiotemporal Disease Mapping Case Event Data Count Data Alternative Models Infectious Diseases Appendix A: Basic R and WinBUGS Appendix B: Selected WinBUGS Code Appendix C: R Code for Thematic Mapping References Index
While concerns about traffic safety were central to the development of conventional community design practice, there has been little empirical examination into the relationship between community design and the incidence of traffic-related crashes, injuries, and deaths. This study examines the relationship between community design and crash incidence. It presents a brief historical review of the safety considerations that helped shape conventional community design practice, followed by the results of negative binomial models developed from a GIS-based database of crash incidence and urban form. The authors find that many of the safety assumptions embedded in contemporary community design practice are not substantiated by the empirical evidence. While it may be true that disconnecting local street networks and relocating non-residential uses to arterial thoroughfares can reduce neighborhood traffic volumes, these community design configurations appear to substitute one set of safety problems for another. Surface arterial thoroughfares, arterial-oriented commercial uses, and big box stores were all found to be associated with an increased incidence of traffic-related crashes and injuries, while higher-density communities with more traditional, pedestrian-oriented retail configurations were found to be associated with fewer crashes. Intersections were found to have a mixed effect on crash incidence. We conclude by discussing the likely reasons for these findings - vehicle operating speeds and systematic design error - and outline three general design considerations that may help address them. © 2013 selection and editorial material, Michael Hibbard, Robert Freestone, and Tore Øivin Sager.
Purpose:
Particulate matter (PM) exposure is linked to inflammation, neuroinflammation, and cognitive decline, whereas aerobic training improves cognition. We investigated the effects of PM exposure during aerobic training on inflammatory biomarkers, serum brain-derived neurotrophic factor (BDNF), an assumed mediator of exercise-induced cognitive improvements, and cognitive performance.
Methods:
Two groups of untrained volunteers completed an aerobic training program of 12 wk, 3 sessions a week: one group (n = 15) in an urban and another group (n = 9) in a rural environment. Ultrafine PM (UFPM) concentrations were measured during each training session. Aerobic fitness (Cooper test), BDNF serum levels, blood total and differential leukocyte counts, exhaled nitric oxide levels, and cognitive performance (Stroop task, Operation Span, and Psychomotor Vigilance task) were analyzed before and after the program.
Results:
UFPM concentrations were significantly higher in the urban environment compared with the rural environment (P = 0.006). Fitness levels improved equally (P < 0.0001) in both groups. Leukocyte counts (P = 0.02), neutrophil counts (P = 0.04), and exhaled nitric oxide levels (P = 0.002) increased after training in the urban group, whereas these parameters did not change in the rural group. The changes in these markers' levels after training showed a positive correlation with the personal average UFPM exposure during training. Reaction times on the Stroop task improved in the rural group (P = 0.001), but not in the urban group. No effects were found on BDNF levels, Operation Span, and Psychomotor Vigilance test performances.
Conclusion:
Aerobic training in an urban environment with high traffic-related air pollution increased inflammatory biomarkers, and, in contrast to aerobic training in a rural environment, cognitive performance on the Stroop task did not improve.
This study proposes a new quadrat method that can be applied to the study of point distributions in a network space. While the conventional planar quadrat method remains one of the most fundamental spatial analytical methods on a two-dimensional plane, its quadrats are usually identified by regular, square grids. However, assuming that they are observed along a network, points in a single quadrat are not necessarily close to each other in terms of their network distance. Using planar quadrats in such cases may distort the representation of the distribution pattern of points on a network. The network-based units used in this article, on the other hand, consist of subsets of the actual network, providing more accurate aggregation of the data points along the network. The performance of the network-based quadrat method is compared with that of the conventional quadrat method through a case study on a point distribution on a network. The χ2 statistic and Moran's I statistic of the two quadrat types indicate that (1) the conventional planar quadrat method tends to overestimate the overall degree of dispersion and (2) the network-based quadrat method derives a more accurate estimate on the local similarity. The article also performs sensitivity analysis on network and planar quadrats across different scales and with different spatial arrangements, in which the abovementioned statistical tendencies are also confirmed.
Road crashes are a major cause of death and injury worldwide. Sophisticated statistical methods are needed to study their distribution and to evaluate the effectiveness of road-safety policies and measures. This paper shows how maps can be used to study the spatial distribution of crashes, to identify data errors, and to plot the results of statistical modelling.
This article shows how the Netherlands, Denmark and Germany have made bicycling a safe, convenient and practical way to get around their cities. The analysis relies on national aggregate data as well as case studies of large and small cities in each country. The key to achieving high levels of cycling appears to be the provision of separate cycling facilities along heavily travelled roads and at intersections, combined with traffic calming of most residential neighbourhoods. Extensive cycling rights of way in the Netherlands, Denmark and Germany are complemented by ample bike parking, full integration with public transport, comprehensive traffic education and training of both cyclists and motor-ists, and a wide range of promotional events intended to generate enthusiasm and wide public support for cycling. In addition to their many pro-bike policies and programmes, the Netherlands, Denmark and Germany make driving expensive as well as inconvenient in central cities through a host of taxes and restrictions on car ownership, use and parking. Moreover, strict land-use policies foster compact, mixed-use developments that generate shorter and thus more bikeable trips. It is the coordinated implementation of this multi-faceted, mutually reinforcing set of policies that best explains the success of these three countries in promoting cycling. For comparison, the article portrays the marginal status of cycling in the UK and the USA, where only about 1% of trips are by bike.
We model the relationship between coronary heart disease and smoking prevalence and deprivation at the small area level using the Poisson log-linear model with and without random effects. Extra-Poisson variability (overdispersion) is handled through the addition of spatially structured and unstructured random effects in a Bayesian framework. In addition, four different measures of smoking prevalence are assessed because the smoking data are obtained from a survey that resulted in quite large differences in the size of the sample across the census tracts. Two of the methods use Bayes adjustments of standardized smoking ratios (local and global adjustments), and one uses a nonparametric spatial averaging technique. A preferred model is identified based on the deviance information criterion. Both smoking and deprivation are found to be statistically significant risk factors, but the effect of the smoking variable is reduced once the confounding effects of deprivation are taken into account. Maps of the spatial variability in relative risk, and the importance of the underlying covariates and random effects terms, are produced. We also identify areas with excess relative risk.
This paper attempts to explain the spatial variation of the use of a bicycle for commuting to work at the level of the 589 municipalities in Belgium. Regression techniques were used and special attention was paid to autocorrelation, heterogeneity and multicollinearity. Spatial lag models were used to correct for the presence of spatial dependence and a disaggregated modelling strategy was adopted for the northern and southern parts of the country. The results show that much of the inter-municipality variation in bicycle use is related to environmental aspects such as the relief, traffic volumes and cycling accidents. Town size, distance travelled and demographic aspects also have some effect. In addition, there are regional differences in the effects of the structural covariates on bicycle use: the impact of variables such as traffic volume and cycling accidents differs substantially between the north and the south of the country. This paper also suggests that high rates of bicycle use in one municipality stimulate cycling in neighbouring municipalities, and hence that a mass effect can be initiated, i.e. more cycle commuting encourages even more commuters in the area to cycle. These findings provide some recommendations for decision-makers wishing to promote a shift from car to bicycle use.
In a survey of 1,402 current and potential cyclists in Metro Vancouver, 73 motivators and deterrents of cycling were evaluated.
The top motivators, consistent among regular, frequent, occasional and potential cyclists, were: routes away from traffic
noise and pollution; routes with beautiful scenery; and paths separated from traffic.In factor analysis, the 73 survey items
were grouped into 15 factors. The following factors had the most influence on likelihood of cycling: safety; ease of cycling;
weather conditions; route conditions; and interactions with motor vehicles. These results indicate the importance of the location
and design of bicycle routes to promote cycling.
KeywordsBicycle-Survey-Infrastructure-Influence-Non-motorized transport