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(A) Not-raining and raining accident risks; (B): Relative risk between not-raining and raining accident risks. Confidence band is 95%.
Source publication
Driven by the high social costs and emotional trauma that result from traffic accidents around the world, research into understanding the factors that influence accident occurrence is critical. There is a lack of consensus about how the management of congestion may affect traffic accidents. This paper aims to improve our understanding of this relat...
Contexts in source publication
Context 1
... risk is the probability that an accident will occur in any particular hour. Looking at congestion level 15 in Figure 5A, for example, the risk of approximately 0.0008 for not-raining accidents means that, while it is not raining, there is a 0.08% chance of an accident occurring at an intersection in the ACC in this congestion level. For both not-raining and raining accidents, the risk of an accident occurring increases with increasing congestion ( Figure 5A). ...
Context 2
... at congestion level 15 in Figure 5A, for example, the risk of approximately 0.0008 for not-raining accidents means that, while it is not raining, there is a 0.08% chance of an accident occurring at an intersection in the ACC in this congestion level. For both not-raining and raining accidents, the risk of an accident occurring increases with increasing congestion ( Figure 5A). ...
Context 3
... raining risks remain higher than not-raining risks ( Figure 5A), the RR becomes smaller ( Figure 5B) as congestion increases. In congestion level one, a RR of approximately five means that the risk of an accident is five times greater when it is raining than when it is not raining. ...
Citations
... Some scholars further considered accidents at intersections. Angus E. Retallack and Bertram Ostendorf [20] studied the correlation between traffic volume and accidents in intersections in Australia. They found a linear relationship at low traffic volumes and a quadratic relationship at high traffic volumes. ...
Crashes involving vulnerable road users (VRUs) are types of traffic accidents which take up a large proportion and cause lots of casualties. With methods of statistics and accident reconstruction, this research investigates 378 actual traffic collisions between vehicles and VRUs in China in 2021 to obtain human, vehicle, and road factors that affect the injury severity. The paper focuses on risky behaviors of VRUs and typical scenarios such as non-use of the crosswalk, violation of traffic lights, stepping into the motorway, and riding against traffic. Then, based on the Bayesian General Ordinal Logit model, influencing factors of injury severity in 168 VRU accidents are analyzed. Results demonstrate that the probability of death in an accident will rise when the motorist is middle-aged and the VRU is an e-bicycle rider; the probability of death in an accident will greatly decrease when the VRU bears minor responsibility. Therefore, middle-aged motorists and e-bicycle riders should
strengthen safety consciousness and compliance with regulations to prevent accident and reduce
injury for VRUs. In addition, helmet-wearing will help to reduce riders’ injuries. This research may
provide ideas for intelligent vehicles to avoid collisions with risky VRUs.
... Traffic accidents are caused by the malfunction of a system, namely, vehicles, road infrastructure, road users, and their interactions [1], and the leading cause of death worldwide and are expected to be the fifth by 2020 [2]. This is a problem because 91 % of fatalities occur on the road [3]. ...
The number of motorized vehicles, especially motor cycles, is also offset by increased traffic accidents. As is known, road accidents essentially depend on four interrelated factors: human behavior, vehicle efficien cy, environmental conditions, and the characteristics of the infrastructure. However, most accidents are attribu table to the first three factors, almost always to impro per user behavior. This study aims to determine motor cyclists' socioeconomic characteristics and conduct on the intensity of accidents. The research location is on the PandaanPurwosari National Road, Pasuruan Regency, Section 094098 (SurabayaMalang). Three hundred forty respondents are motorcyclists who have experi enced accidents in this segment. The research method is interviews and questionnaires-data analysis using Structure Equation Modeling (SEM), with software SmartPLS (Partial Least Square). The result of accident modeling Y = 0.299X 1 +0.154X 2 + +0.077X 3 +0.554X 4. The first biggest influence on the chance of an accident is the characteristics of driving behavior (X4) exceeding speed (X4.10). The more often the rider exceeds the rate, the higher the chance of an ac cident. The second most significant influence of socioeco nomic characteristics (X1) is the age indicator (X1.2), the more mobility in the productive age, the higher the risk of accidents
... Loop R2 focuses mainly on the influence of increased congestion in loop B3 on traffic accidents, as these impose great economic and social costs on communities. Estimating the congestion-speed-crash relationship has long focused on roadway safety analysis [54,55]. Although there is an inverse relationship between accidents and congestion, it would imply a benefit of congested conditions for road safety. ...
The Australian Sustainability Development Goals (SDGs) Summit in 2018 attracted much-needed national attention towards environmental goals and targets compared with other aspects of sustainability. Road infrastructure is the backbone of modern society and plays a crucial role in accomplishing a targeted balance between these aspects of sustainability and achieving the SDGs. This article presents an integrated sustainability performance assessment methodology that acts as a decision support tool. A series of two conceptual modelling techniques-drivers-pressure-state-impact-response (DPSIR) and system dynamics (SD)-is employed, with the cause-and-effect relationships of the sustainability indicators developed utilising the DPSIR framework, and a quantitative analysis carried out through a subsequent SD model. The end result is the generation of a Sustainability Performance Index (SPI) for road infrastructure created by analysing the SD model and DPSIR index layer relationship. The benefits and applicability of the proposed methodology are validated through case study analysis. The overall aim is to determine restricting factors and response strategies influencing road infrastructure and transport sustainability performance during the operation and maintenance phase. Thus, a significant contribution is made through the proposed methodology for assessing factors influencing the long-term achievement of the SDGs.
... The meta-analysis study by Hoye et al. [6] reveals that traffic crashes increase with an increase in traffic volume. Retallack et al. [7] report that at lower traffic volumes, a linear relationship exists between traffic volume and crash frequency, and at higher traffic volumes, a quadratic relationship exists between the two. Ahangari et al. [8] show that the two factors of vehicle miles travelled and vehicles per capita have the strongest impact on traffic fatality rates, and a study by Segui-gomez et al. [9] reveals that even a small reduction in the number of kilometres travelled, contributes significantly to protection from crashes and resulting injuries. ...
This study investigates the important role of attendant factors, such as road traffic victims’ access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels–Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave’s partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury—Grievous as against Road Traffic Injury—Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents.
... On the one hand, it could be explained by the increasing number of sudden lane-changing related crashes, accounting for approximately 17% of severe accidents [84]. On the other hand, various pieces of literature have identified a positive correlation between traffic volume and accident frequency [85,86]. In addition, excessive speed driving could be more frequent on roads with a higher speed limit, which also contributes to higher crash frequency. ...
Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency.
... Unbiased analyses were carried out in order to determine which variables had a significant impact on conflict. Lanes 1 and 2 predict data with a 78% and 85% accuracy respectively [6][7][8][9][10]. ...
... And after comparing all the pedestrians to vehicles in tables (5), (6) pedestrians speed is much more when they passes first w.r.to two wheeler, having less speed when pedestrian passes first w.r.to three wheeler. 4. From the tables (5), (6) I can conclude that SSD is greater than the SD for VPF compared to PPF so it very critical to pass safely when VPF. 5. Severities are evaluated from the trajectory data and severity levels are analysed based on the severities of the junction as shown in the tables (7), (8). 6. ...
As the pedestrian’s rapid progress, pedestrian safety has recently assumed greater importance in the research population activities. To assess road user interactions at an unsignalized junctions with heterogeneous traffic complexity, innovative trajectory-based data was used and to make urban intersections safer for road users, the proposed severity levels will be used to test and evaluate numerous infrastructures and control upgrades. Study authors suggests an advanced pattern-based technique to characterize pedestrian-vehicle interactions based on road user behaviors. Surrogate safety measures (SSM) can be estimated more accurately with this study than with the regular grid-based analysis. In order to evaluate SSM (Speed, Time to Collision (TTC), Gap Time of the pedestrian and vehicle interactions) at an unsignalized crossing, Safe Distance and Stopping sight distance with trajectory data are to be estimated. Support Vector Machine (SVM) is used to classify severity grades based on specified indicators generated at an unsignalized junction in India. From the analysis, Severity at the intersection found as 8.12, 8.91sec respectively, Average gap time, stopping sight distance also calculated. Plots between Time to Collision and Gap Time for pedestrian passing first (PPF), vehicle passing first (VPF) are compared by developing a linear regression model and R ² =0.684, 0.656 developed with some independent parameters respectively. Concluded that TTC for PPF is higher than VPF and by considering all the evaluated values this research had improved the methods for analysing and improving the safety of uncontrolled intersections.
... It is worth noting that in this research we calculate the number of accidents per number of vehicles. In [55] authors analyzed real data of 120 intersections. They found a linear (no-linear) relationship between traffic volume and the number of accidents at lower (higher) traffic volumes. ...
In this paper, we propose a two-lane cellular automata model that explains the relationship between traffic-related parameters and likelihood of accidents Pac at a signalized intersection. It is found that, the risk of collision rises as well as the lane-changing probability Pchg augments, besides, the accidents and inflow α show a nonlinear relationship. Moreover, Pac exhibits three different phases (I, II and III) depending on α. Likewise, the system exhibits a second (first) order transition from phase I to phase II when Pchg>0 (Pchg=0). Nevertheless, the transition from phase II to phase III is of first (second) order when Pchg>0 (Pchg=0). In addition, the result analysis shows that the distribution of accidents according to the intersection sites is not equiprobable. Furthermore, when the traffic arriving strength is not very high, the green light length of one road should be increased to restrain Pac and enhance the road safety. Finally, the model results illustrated that the traffic at the intersection is more dangerous adopting asymmetric lane-changing rules than symmetric ones.
... In the work of Retallack et al. (2020), it was revealed that there is a relationship between the volume of traffic and the occurrence of accidents at points of intersection. Although the importance of traffic volume data for the analysis of road accidents is recognized, such data are collected only at 20 points throughout the state of Goiás. ...
Traffic accidents have awakened a global concern as they are the ninth leading cause of death worldwide. This triggers a challenge for traffic managers and planners interested in ensuring the fluidity of traffic and also road safety. In this context, spatial analyzes are essential to recognize critical points or stretches on the road and thus contribute to decisions aimed for the reduction of traffic accidents. In light of the above, this study aims to carry out spatiotemporal analyzes of hotspots of accidents occurred on federal highways located in the state of Goiás, central region of Brazil, in the period from 2014 to 2019. In order to achieve the goal of this work, the available data (from the website of the Federal Highway Police of Brazil) was geocoded in a Geographic Information System (GIS) program and the descriptive statistical analysis was emplyed. The results showed that, in general, accidents occur at points of intersection. In addition, the stretch from Aparecida de Goiânia to Alexânia, in the surrounding area of Brasilia, capital of Brazil, in all the studied years, presented a higher density of accident occurrences, with a high amount of fatal outcomes. Therefore, this stretch of road has become crucial, even with the best traffic conditions.
... Beyond individual differences like an ADHD diagnosis, complexity of the road traffic environment also greatly influences child pedestrian injury risk [27]. With increasing traffic volume and speed, the rate of RTIs increases substantially for child pedestrians [28,29]. ...
All children are vulnerable to pedestrian injuries, but previous research suggests children diagnosed with ADHD may have elevated risk. Child pedestrian injury risk also increases with increasing traffic volume and speed. The current study examined three hypotheses: (a) Pedestrian behavior of children with ADHD is riskier than that of typically-developing children; (b) Children’s pedestrian behavior is riskier with increased traffic complexity; and (c) Pedestrian behavior of children with ADHD is influenced more by complex traffic situations than behavior of typically-developing children. A sample of 38 children ages 8–12 years, 45% diagnosed with ADHD, completed 21 virtual street-crossings, 7 in each of three levels of traffic complexity. Outcome measures included unsafe crossings, ratio of looking at traffic by time, start-delay to enter the road, time to contact with oncoming vehicles, and time waiting to cross. A repeated measure MANOVA and follow-up tests showed that all children had more unsafe crossings, shorter start-delays and shorter TTCs when exposed to increased traffic complexity compared to lighter traffic. Children with ADHD had more unsafe crossings than typically-developing children. Further, compared to typically-developing children, ADHD children had comparatively more unsafe crossings, lower time to contact and longer wait-time in more complex traffic environments. Executive function deficits among children with ADHD likely influence their behavior in complex traffic environments. Implications of the results for policy-making and preventive strategies are discussed.
... Similarly, the authors in [71] found this to be the case on two to three-lane motorways in France. However, this is not true at intersections [72], or on urban roads in London [51,71], where the number of accidents was found to increase linearly at low to mid-levels of traffic and nonlinearly at high levels of traffic. ...
... This study also suggests that lower traffic density on average leads to fewer collisions regardless of adherence levels, as was observed in [72]. However, as adherence decreases, this leads to increased collisions relative to the number of vehicles in the urban environment. ...
... This study's results do not reflect the same linear-to-nonlinear relationship between accidents and traffic levels as [71,72]. At low to mid traffic densities, collisions increase disproportionately as traffic density increases. ...
Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers are more likely to break local and fixed speed limits in urban environments. Analysis conducted by the Department for Transport found that the UK’s accident prevention measure’s cost is estimated to be £33bn per year. Therefore, there is a strong motivation to investigate the causes of roadside collisions in urban environments to better prepare traffic management, support local council policies, and ultimately reduce collision rates. This study utilises agent-based modelling as a tool to plan, experiment and investigate the relationship between speeding and vehicle density with collisions. The study found that higher traffic density results in more vehicles travelling at a slower speed, regardless of the degree to which drivers comply with speed restrictions. Secondly, collisions increase linearly as speed compliance is reduced for all densities. Collisions are lowest when all vehicles comply with speed limits for all densities. Lastly, higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels, increasing the likelihood of congestion around these sites. This work, when extended to real-world applications using empirical data, can support effective road safety policies.
