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Evaluation of Effectiveness of Stop-Sign Treatment at Highway–Railroad Grade Crossings
Abstract
The safety benefit of stop-sign treatment employed at passive highway–rail crossings has been a subject of research for many years. The objective of this study is to assess the effectiveness of the stop-sign treatment on crossing safety. Using the Federal Railroad Administration database, the research focused on 26 years of vehicle–train accident history in the United States from 1980 through 2005. A before-and-after and cross-sectional statistical analysis was conducted for 7,394 public highway–railroad grade crossings that were upgraded from cross buck only to stop signs without involvement in other traffic-control devices (TCDs) or automatic countermeasures. The study found that accident rates based on annual accident frequency per 1,000 crossings were significantly higher during the period when crossings were controlled by cross bucks only than when they were controlled by stop signs. Further, this study developed negative binomial accident prediction models, respectively, for paved and unpaved highway–rail grade-crossings that include effect for stop-sign treatment. Based on specific attributes of the current crossbuck-only-controlled crossings, decision makers and traffic engineers can use the models to examine the accident risks at crossings and assess the potential effectiveness of stop-sign treatment.
... A large body of research has been conducted to assess risk factors for HRGC safety that pertain to driver behavior or operational characteristics of the roadway. Higher traffic volumes, higher train speeds, and a greater percentage of large trucks at the crossing have all been found to be associated with higher rates of fatal and serious injury crashes (Abraham et al., 1998;Lee et al., 2019;Millegan et al., 2009;Raub, 2009). Another characteristic found to be associated with higher serious injury crash rates includes driver age (Abraham et al., 1998;Raub, 2009). ...
... A number of different types of passive treatment devices have been studied for safety effectiveness at HRGCs. Stop signs are a commonly used treatment, and one comprehensive national study byMillegan et al. (2009) found significantly lower crash rates at HRGCs treated only with Stop signs when compared to crossings treated only with crossbucks. Other studies have found similar benefits of Stop sign installation in addition to or instead of crossbucks (Yan ...
A short storage space railroad crossing has insufficient distance between the crossing and the highway intersection stop line to safely store a design vehicle. The current Michigan Department of Transportation (MDOT) project (OR 19-032), led by Texas A&M Transportation Institute (TTI), aimed to provide guidance on these crossing types. This study examined the crash data, surroundings of the crossing, and driver behavioral patterns using naturalistic driving and simulation data to identify suitable countermeasures and build communication between MDOT Traffic & Safety and Office of Rail to refine the guidelines. This project conducted four major tasks: 1) a comprehensive review on short-storage crossing related studies, 2) data preparation and development of safety indices for short-storage crossing locations, 3) development of driver behavior related safety scoring for different passive treatments using data from SHRP-2 naturalistic driving study (NDS) and simulation, and 4) development of a guidance document and final report on short-storage crossings in Michigan. The findings of literature review show that little research has been conducted to empirically evaluate the effectiveness of different treatments for grade crossings with short
... During 1981–2013, the number of crashes at highway-rail grade crossings has reduced by 77.8% (FRA, 2013 ). The decreases are typically attributed to the upgrading from passive to active crossings and the improvements made on active grade crossings (Meeker et al., 1997; Millegan et al., 2009; Lenné et al., 2011). Compared with passive controls (STOP signs and Crossbucks), active control devices (flashing lights and gates) have shown lower crash rates (Raub, 2009). ...
... Many studies have compared crash rate at passive controls and active controls (Austin and Carson, 2002; Elvik and Vaa, 2004; Mok and Savage, 2005; Park and Saccomanno, 2005; Raub, 2006; Saccomanno et al., 2007; Elvik et al., 2009; Millegan et al., 2009; Raub, 2009; Yan et al., 2010a,b). They have generally found that crash rates are lower at STOP controlled intersections compared with Crossbuck signs. ...
... Because crash frequency data are nonnegative integer-valued random data, Poisson and negative binomial regression models have been commonly applied for grade-crossing safety analysis and prediction (Austin and Carson, 2002;Park and Saccomanno, 2005;Millegan et al., 2009). As extensions of Poisson and negative binomial models, zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models have also been used (Nam and Lee, 2006;Oh et al., 2006;Hu and Lee, 2008), accounting for crashes that are so low in frequency over some time period that they can be considered as a zero-accident state (i.e., many zero crash frequencies in the data). ...
... However, considering the whole picture of at-grade crossing safety in U.S., crossings are getting safer and safer over years due to improvement in vehicle design, drivers' safety awareness, and other factors. In a prior study by Millegan et al. (2009), the effect of year based on annual accident rates for the crossings that are controlled by crossbucks-only or stop sign was a heuristically explored. The annual accident rate was calculated as the total number of accidents occurring divided by the number (in thousands) of crossings in a particular year. ...
... Effectiveness of a specific countermeasure is defined as the percent reduction in accidents after installation of the countermeasure (Farr, 1987). Methods to measure countermeasure effectiveness include statistical tools (Park and Saccomanno, 2005;Saccomanno and Lia, 2005;Raub, 2006;Washington and Oh, 2006;Saccomanno et al., 2007;Yan et al., 2010), before and after studies (Dommasch et al., 1976;Bowman, 1987;Heathington et al., 1989;Ward and Wilde, 1995;Noyce and Fambro, 1998;Ko et al., 2007;Millegan et al., 2009Millegan et al., , 2009Millegan et al., , 2010, simulation Moon, 1996, 1997;Rudin-Brown et al., 2012), data comparison and on-site observations (Wortman et al., 1972;Wortman and Lipinski, 1974;Lipinski and Wortman, 1976;Russell et al., 1979;Morrissey, 1980;Farr and Hitz, 1984;Eck and Halkias, 1985;Mather, 1991;Burnham, 1995;Russell and Rys, 1996;Raub, 2006;Hellman et al., 2007). The Railroad-Highway Grade Crossing Handbook 2007 of US DOT FHWA (Ogden, 2007) provides a detailed discussion on definition of countermeasures, minimum installation requirements and guidance to selection of countermeasure alternatives for AGRHXs. ...
... Effectiveness of a specific countermeasure is defined as the percent reduction in accidents after installation of the countermeasure (Farr, 1987). Methods to measure countermeasure effectiveness include statistical tools (Park and Saccomanno, 2005;Saccomanno and Lia, 2005;Raub, 2006;Washington and Oh, 2006;Saccomanno et al., 2007;Yan et al., 2010), before and after studies (Dommasch et al., 1976;Bowman, 1987;Heathington et al., 1989;Ward and Wilde, 1995;Noyce and Fambro, 1998;Ko et al., 2007;Millegan et al., 2009Millegan et al., , 2009Millegan et al., , 2010, simulation Moon, 1996, 1997;Rudin-Brown et al., 2012), data comparison and on-site observations (Wortman et al., 1972;Wortman and Lipinski, 1974;Lipinski and Wortman, 1976;Russell et al., 1979;Morrissey, 1980;Farr and Hitz, 1984;Eck and Halkias, 1985;Mather, 1991;Burnham, 1995;Russell and Rys, 1996;Raub, 2006;Hellman et al., 2007). The Railroad-Highway Grade Crossing Handbook 2007 of US DOT FHWA (Ogden, 2007) provides a detailed discussion on definition of countermeasures, minimum installation requirements and guidance to selection of countermeasure alternatives for AGRHXs. ...
... Because crash frequency data are nonnegative integer-valued random data, Poisson and negative binomial regression models have been commonly applied for grade-crossing safety analysis and prediction (Austin and Carson, 2002;Park and Saccomanno, 2005;Millegan et al., 2009). As extensions of Poisson and negative binomial models, zero-inflated Poisson (ZIP) and zeroinflated negative binomial (ZINB) regression models have also been used (Nam and Lee, 2006;Oh et al., 2006;Hu and Lee, 2008), accounting for crashes that are so low in frequency over some time period that they can be considered as a zero-accident state (i.e., many zero crash frequencies in the data). ...
... However, considering the whole picture of at-grade crossing safety in U.S., crossings are getting safer and safer over years due to improvement in vehicle design, drivers' safety awareness, and other factors. In a prior study by Millegan et al. (2009), the effect of year based on annual accident rates for the crossings that are controlled by crossbucks-only or stop sign was a heuristically explored. The annual accident rate was calculated as the total number of accidents occurring divided by the number (in thousands) of crossings in a particular year. ...
This paper applies a nonparametric statistical method, hierarchical tree-based regression (HTBR), to explore train-vehicle crash prediction and analysis at passive highway-rail grade crossings. Using the Federal Railroad Administration (FRA) database, the research focuses on 27 years of train-vehicle accident history in the United States from 1980 through 2006. A cross-sectional statistical analysis based on HTBR is conducted for public highway-rail grade crossings that were upgraded from crossbuck-only to stop signs without involvement of other traffic-control devices or automatic countermeasures. In this study, HTBR models are developed to predict train-vehicle crash frequencies for passive grade crossings controlled by crossbucks only and crossbucks combined with stop signs respectively, and assess how the crash frequencies change after the stop-sign treatment is applied at the crossbuck-only-controlled crossings. The study results indicate that stop-sign treatment is an effective engineering countermeasure to improve safety at the passive grade crossings. Decision makers and traffic engineers can use the HTBR models to examine train-vehicle crash frequency at passive crossings and assess the potential effectiveness of stop-sign treatment based on specific attributes of the given crossings.
... Stratified tree-based regression was reported to be useful in analyzing and forecasting crashes at passive RRGCs ). Millegan et al. (2009) reported that negative binomial regression could be utilized for developing alternate prediction models. Tjahjono et al. (2019) analyzed four years of crash data using the ordered probit and logit model and revealed that waterlogged surfaces, two-wheelers, low traffic volumes, and male drivers are among the prime promoters responsible for fatal accidents at RRGCs. ...
Introduction:
Railroad grade crossings (RRGCs) have emerged as critical transportation infrastructures from the point of safety and operational aspects because two modes of transportation intermingle at the intersecting zone; the understanding of safety and traffic operation at RRGC is of prime concern while planning and designing this transportation facility.
Method:
In this context, this work tries to comprehend RRGC performance-related parameters from published literature and figure out critical gaps. An international synthesis on the identified potential parameters influencing the RRGC performance (i.e., safety, driver behavior, and operational impact) was carried out by critically reviewing the articles published worldwide. Furthermore, key findings, used variables, analysis methods, research gaps, and recommendations were studied.
Results:
The review revealed that many researchers had explored the driver behavior and safety aspect based on past crash data and violations prevailing at RRGC. However, little research has been done to evaluate the effect of highways' operational characteristics on the performance of RRGC. Moreover, limited investigation has been carried out to understand the dilemma of drivers and the proactive safety evaluation of pedestrians and non-motorized vehicles at RRGC. A total of seven critical research gaps concerning parameters are recognized, facilitating a clear agenda for further research pertaining to RRGC performance.
... Other characteristics associated with higher serious injury crash rates include driver 95 age (Raub, 2009;Abraham et al., 1998) and time of day Salim, 2018). Several studies have found the speed 96 of the vehicle approaching the grade crossing to be significantly associated with crash risk; that is, higher levels of travel 97 speed are found to be associated with an increased risk of a crash or greater crash severity (Millegan et al., 2009). This 98 may be due to the inability of the driver to accurately gauge safe stopping distances at higher speeds, along with the height-99 ened difficulty of processing multiple external stimuli for correct decision making. ...
Crashes at highway rail grade crossings (HRGCs) are often involved with fatalities due to the momentum of a train. This study collected nine years (2010-2018) of fatal HRGC crashes from the Fatality Analysis Reporting System (FARS) to perform the analysis. The Taxicab Correspondence Analysis (TCA) was applied to this dataset. This method identified several patterns that trigger HRGC-related fatal crashes. The findings indicate that fatal crashes involving multiple fatalities are often highly associated with alcohol-influenced drivers, poor lighting conditionz, and inclement weather. The fatal crash that occurs during the daylight with the uninfluenced driver is less likely to involve more than one fatality. The results also recognized the combinations of vehicle type and speed are associated with fatal crashes at rail grade crossings. The relatively low-speed limit crossings and larger utility vehicles are more likely to be associated with fatal crashes because large vehicles require a longer time to cross railroads at a low speed. The relatively high-speed limit crossing and smaller or lighter vehicles, especially the motorcycle, are highly associated with fatal crashes.
[International Journal of Transportation Science and Technology]
... Relatively little research effort has focused on HRGC accidents compared to roadway accidents [15][16][17][18][19][20][21][22][23][24]. Moreover, among all the previous HRGC accident analyses, the majority of them focus only either on crash frequency, often based on crossing inventory databases [19,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], or on crash severity analysis, often based on historical crash police report databases [16,[40][41][42][43][44][45][46][47][48][49][50][51][52]. To understand and predict crash frequency and severity simultaneously and consistently is important for agencies seeking to improve safety so they can account for the common factors affecting both crash frequency and severity. ...
This paper proposes a mathematical model, the competing risks method, to investigate highway-rail grade crossing (HRGC) crash frequency and crash severity simultaneously over a 30-year period. The proposed competing risks model is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest; in this case, the competing multiple outcomes are crash severities, while event of interest is crash occurrence. Knowledge-gain-based benefits to be discovered through the application of this model and 30-year dataset are as follows: (1) a straightforward and integrated one-step estimation process that considers both crash frequency and severity likelihood in the same model, so direct hazard ranking considering both crash frequency and severity likelihood is possible; (2) interpretative effects of identified covariates from both the direction and magnitude perspectives; and (3) the long-term cumulative effect of contributors with the cumulative incidence function.
... According to Saccomanno et al. (2007) the strongest countermeasure effects in reducing RLCs were in upgrading the warning device from 2-to 4-quadrant gates and installing photo/video enforcement, while the lowest effect was related to introducing yield signs ahead of RLCs. Millegan et al. (2009) assessed the effectiveness of stop-sign placement on crossing safety before and after upgrades from cross buck only to stop signs, without consideration of other countermeasures. For overall safety improvement at RLCs, Silmon and Roberts (2010) presented the potential benefits of introducing obstacle detection systems on automatic half-barrier level crossings (AHB). ...
Railway level crossings (RLCs) are critical points characterized by a large number of accidents per year due to the intersection of railway and roadway infrastructures. The improvement of safety at RLCs is of crucial importance worldwide. This paper proposes a new approach for evaluating safety at RLCs using non-radial DEA (Data Envelopment Analysis) model. The introduced non-radial DEA model is employed for the evaluation of railway efficiency of European countries regarding the level of safety at RLCs through considering desirable and undesirable inputs, as well as desirable and undesirable outputs for the period from 2010 to 2012 and for the year 2014. Due to missing the comprehensive set of data for roadway traffic, the evaluation of safety at railway RLCs has been conducted from railway viewpoint. Through the application of modified non-radial DEA model, results indicate the most efficient countries, as well as countries with a lower efficiency of railway performance in terms of the level of safety at RLCs. In order to check the validity of modified non-radial DEA model, comparison of results and sensitivity analysis were conducted. Despite the validity, results of the sensitivity analysis indicated also some weaknesses of the modified non-radial DEA model related to missing and inaccurate data, the number of variables included, and the selection of inputs and outputs. A modified non-radial DEA model, as proposed in the paper, with overall set of data can be used for evaluating the efficiency of safety improvement at RLCs, efficiency of different countermeasures before and after implementation at RLCs of different.
... Some studies have also examined crossing behaviour of cyclists at intersections and roads (Ducheyne, De Bourdeaudhuij, Lenoir, & Cardon, 2014;Feenstra, Ruiter, Schepers, Peters, & Kok, 2011;Schepers, Twisk, Fishman, Fyhri, & Jensen, 2015), while a handful of studies have looked at both pedestrians and cyclists (Beanland, Lenne, Salmon, & Stanton, 2013;Khattak & Luo, 2011;Lange, Haiduk, Schwarze, & Eggert, 2011). Cushing-Daniels and Murray (2005) reported that increased use of a train whistle at LCs may increase safety but at high costs to nearby homeowners, while a few studies (Millegan, Yan, Richards, & Han, 2009;Wullems, 2011;Yan, Han, Richards, & Millegan, 2010) reported varying degrees of risk reduction using warning devices and stop signs. While numerous safety measures have been proposed to reduce risky pedestrian behaviour (Siques, 2002), researchers have called for a more systematic approach to understand and predict such behaviour (Read et al., 2013;Salmon et al., 2015). ...
Given the high risk of severe accidents at level crossings (LCs), this study examined legal and illegal crossings by pedestrians and cyclists at a high-traffic LC in Zagreb, Croatia. Survey data and field observations were collected to identify reasons for risky behaviour. Behaviour was observed under normal conditions and in the presence of various safety measures in order to identify measures that can reduce risky behaviour. Results show that the presence of police officer at the LC was most effective at reducing illegal crossings, while the presence of cameras contributes significantly as well, especially after safety educational campaign when illegal crossing decreases for 59.23%. We can assume that in future the improvement regarding human behaviour on LC could be made with cameras on LC and more frequent educational campaigns. This is the first reported use of field survey and video surveillance methods to analyse user behaviour at LCs in Croatia.
... Measurements that are previously unknown are now available, which include but not be limited to: vehicle speeds, positions, arrival rates, rates of acceleration and deceleration, queue lengths, stopped time and complex information about the instantaneous driving contexts (location, road condition, traffic control device, etc.) [43][44][45]. These, referred to as big data, are valuable information that can generate significant traffic safety benefits when mined properly [44][45][46][47]. A great number of studies have investigated the benefits from an overall prospective, based an assumption that the correlates of traffic safety outcomes are stationary [26,48,49,55,61,63,78,81]. Few studies have been focused on exploring the non-stationary correlates, because of the unavailable location data. ...
Future vehicle warning systems needs a local (instead of global) analysis of real-time information transmitted between vehicles and infrastructures, to provide local warning information matching the instantaneous driving contexts. Spatial modeling techniques extracting the location information into the analysis fulfill the needs of local analysis. For truck-involved collisions at highway-rail crossings, the local warnings seem to matters more, than for traffic crashes at the normal highway segments and regular intersections. Crashes at rail grade crossings can result in severe injuries and fatalities to vehicle occupants, while truck-involved crashes at crossings can further result in serious damage to train, crossing and railway equipment. Truck-involved crashes at grade crossings have received limited attention compared with crashes involved with passenger cars. This study presents a methodology of improving safety of trucks at railroad crossings, by taking advantage of the big data containing location information. Unlike previous studies that constructed a direct relationship between the safety outcomes and associated factors, this study investigates direct relationships together with indirect relationships through the truck driver behaviors before collisions, using path analysis techniques. To sum up, this study applies a spatial approach fused with path analysis to uncover the local relationships between truck driver injury severity and crossing controls across the space. By doing so, the research is able to: i) provide a benchmark of identifying potentially risky vehicles on a real time basis; ii) evaluate current control devices at railroad crossing across the country and pin point the potentially problematic crossing sites. An empirical study was conducted by using a rich crash database from the Federal Railroad Administration (N= 4738 for 2004-2014). The results show that truck-involved crashes occurring at crossings without gate controls are generally associated with higher chance of injury, while the associations vary significantly across the space. In general, crashes in the Midwest and Great Lake regions are associated with an even higher chance of injury at crossings without gates, compared with other regions. More Results and implications are discussed in the paper.
... Although crash avoidance systems (Lewis, 2002;Tang & Yip, 2010) have been improved, crashes still occur (Oestern et al., 2000;Nagata et al., 2006), often resulting in mass-casualty incidents so grave that they can be classified as disasters (≥10 killed and/or A Study of a Mass Casualty Train Crash 153 ≥100 nonfatally injured) (Forsberg & Björnstig, 2011). Level-crossing crashes are the most common cause of train crashes despite efforts to improve existing-level crossings (Millegran et al., 2009;Yan, 2010) and to refrain from building new ones. However, despite these efforts the number of level-crossing crashes has remained the same in relation to the number of passenger kilometers travelled between 1990 and 2009 in Europe (Evans, 2011). ...
This case study includes 73 fatally and nonfatally injured passengers from a level-crossing train crash in Nosaby, Sweden, in 2004. The aim was to identify the injury panorama and the injury objects and to determine the injury-inducing variables. Data were analyzed with descriptive statistics, quantitative content analysis, and multivariate data analysis. The first carriage overturned, and its occupants were the ones who suffered the most severe and lethal injuries. Injury type and injury location for these passengers also differed significantly from the passengers on the other two carriages. Tables significantly influenced injury type and injury location in all carriages, whereas the injured persons’ seating position had significant effect only in the second and third carriage. Those who had travelled facing forward with a table in front of them in Carriages 2 and 3 were more likely to have sustained injuries to their abdomen or pelvis. Other injury-inducing objects were seats, interior structures, wood pellets from the truck, and other passengers. Neck sprains were significantly more prevalent among those who had travelled facing backward. Improved train crashworthiness also needs to include interior safety, which would have a potential to reduce crash injuries.
... (C.M. Rudin-Brown). a lower accident frequency at level crossings with stop signs compared to cross bucks alone (Millegan et al., 2009), other studies have found the opposite trend, with stop signs being associated with the highest fatality rate compared to cross bucks or flashing lights (Raub, 2006). Since the early 1970s, in an effort to improve road safety, Australian and international railway authorities have made concerted efforts to reduce the number of road-rail level crossings, particularly those protected by passive devices Horton et al., 2009). ...
Although collisions at level crossings are relatively uncommon occurrences, the potential severity of their consequences make them a top priority among safety authorities. Twenty-five fully-licensed drivers aged between 20 and 50 years participated in a driving simulator study that compared the efficacy, and drivers' subjective perception, of two active level crossing traffic control devices: flashing lights with boom barriers and standard traffic lights. Because of its common usage in most states in Australia, a stop sign-controlled level crossing served as the passive referent. Although crossing violations were less likely at the level crossings controlled by active devices than at those controlled by stop signs, both kinds of active control were associated with a similar number of violations. Further, the majority (72%) of drivers reported preferring flashing lights to traffic lights. Collectively, results indicate that the installation of traffic lights at real-world level crossings would not be likely to offer safety benefits over and above those provided already by flashing lights with boom barriers. Furthermore, the high rate of violations at passively controlled crossings strongly supports the continued practice of upgrading level crossings with active traffic control devices.
Highway-rail grade crossing (HRGC) safety is one of the priority areas in the United States transportation system that requires for greater research efforts not just limited to crash analysis, but also to gain a deeper understanding of surrogate safety measures such as driver behavior-based traffic violations at HRGCs. This paper uses vehicle profile data to identify the key variables and develop prediction models for gate violations and examine the relationship between model accuracy and the key input variables. A data set of 256 vehicle-train events was collected at two HRGC testbeds in Lincoln, Nebraska. Among them, 76 events are gate violations, and 180 events are non-violations. Two tree-based ensemble techniques, the bootstrap forest and the boosted tree, were applied to the data set. It was found that once a vehicle is within 190 feet from the HRGC stop line, the model was approximately 80 percent accurate in predicting a gate violation. It was also found that as vehicles came closer to the HRGC, the prediction error decreased. With the advent of vehicle profile data collection, tree-based ensemble techniques are ideal for safety studies as they can utilize the highly non-linear vehicle profiles and relate these to safety surrogate metrics.
As societies become more complex and interconnected, the global risk for catastrophic disasters is increasing. Demand for expertise to mitigate the human suffering and damage these events cause is also high. A new field of disaster medicine is emerging, offering innovative approaches to optimize disaster management. Much of the information needed to create the foundation for this growing specialty is not objectively described or is scattered among multiple different sources. This definitive work brings together a coherent and comprehensive collection of scientific observations and evidence-based recommendations with expert contributors from around the globe. This book identifies essential subject matter, clarifies nomenclature, and outlines necessary areas of proficiency for healthcare professionals handling mass casualty crises. It also describes in-depth strategies for the rapid diagnosis and treatment of victims suffering from blast injuries or exposure to chemical, biological, and radiological agents.
Introduction:
Highway-rail at-grade crossings (HRGCs) are critical locations where a railway and a roadway intersect with one another. Crashes at those locations often result in fatalities and economic and social damages due to the impacts on both road and rail users. The main purpose of countermeasures at HRGCs is to permit safe and efficient rail and highway operations.
Method:
Countermeasures at highway-rail grade crossings (HRGCs) considered in this study include all traffic control devices and other warning and barrier devices at or on approaches to crossings. In general, active devices are commonly accepted as more effective countermeasures than passive devices. However, many of the previous effectiveness studies are either at the project level or were conducted without considering the before-improvement condition. This study focuses on the network-level marginal effectiveness of countermeasures on crash rate and severity levels during the 29-year study period from 1990 to 2018 by fully considering before-improvement control levels. A competing risk model (CRM) is able to accommodate the competing nature of crash severities as multiple outcomes from the same event of interest, which is crash occurrence in this study. Subsequently, CRM is used in this study as an integrated one-step estimation approach that investigates both crash frequency and severity likelihood over time.
Results:
The study findings indicate that adding audible devices to crossings already equipped with gates will result in a considerable annual decline in crash occurrence likelihood (0.25%). The same device installed at crossings already controlled by gates and flashing lights results in less reduction in crash occurrence likelihood of 0.14%. Moreover, adding a stop sign to the active crossing controls of gates, standard flashing lights, and audible devices will lead to a decrease in the probability of crash occurrence and severe crashes (injury and fatal). However, adding stop signs to crossings equipped only with crossbucks will increase the crash occurrence.
This paper proposes a machine learning approach, the random survival forest (RSF) for competing risks, to investigate highway-rail grade crossing (HRGC) crash severity during a 29-year analysis period. The benefits of the RSF approach are that it (1) is a special type of survival analysis able to accommodate the competing nature of multiple-event outcomes to the same event of interest (here the competing multiple events are crash severities), (2) is able to conduct an event-specific selection of risk factors, (3) has the capability to determine long-term cumulative effects of contributors with the cumulative incidence function (CIF), (4) provides high prediction performance, and (5) is effective in high-dimensional settings. The RSF approach is able to consider complexities in HRGC safety analysis, e.g., non-linear relationships between HRGCs crash severities and the contributing factors and heterogeneity in data. Variable importance (VIMP) technique is adopted in this research for selecting the most predictive contributors for each crash-severity level. Moreover, marginal effect analysis results real several HRGC countermeasures’ effectiveness. Several insightful findings are discovered. For examples, adding stop signs to HRGCs that already have a combination of gate, standard flashing lights, and audible devices will reduce the likelihood of property damage only (PDO) crashes for up to seven years; but after the seventh year, the crossings are more likely to have PDO crashes. Adding audible devices to crossing with gates and standard flashing lights will reduce crash likelihood, PDO, injury, and fatal crashes by 49 %, 52 %, 46 %, and 50 %, respectively.
Introduction:
Data from the Federal Railroad Administration show high numbers of incidents at the approximately 210,446 highway-railroad grade crossings across the United States. One cause for this unsettling trend is the problem of drivers stopping within the dynamic envelope zone (DEZ) of the train while in queue. This research seeks to study DEZ stopping behavior at highway-railroad grade crossings by evaluating regulatory signage and further analyze variables that may affect this behavior.
Method:
A comparative safety analysis is undertaken to evaluate the effectiveness of the standard "Do Not Stop on Tracks" (R8-8) sign by using percentage change calculations and chi-squared tests. The study then conducts a market basket analysis (MBA) to extrapolate on these results and to identify underlying factors associated with observed driver behavior using variables influenced by visibility, perception, and maneuverability.
Results:
Rather low reductions in safety violations resulted from the R8-8 installation, including a 2.2% reduction in DEZ stopping behavior and only a slight 3.7% increase in compliance. The results of the MBA identified associations that affirmed assumptions about driver behavior, while other associations were not as direct but altogether helped broaden the understanding of interactions at grade crossings.
Conclusions:
This study concluded that the R8-8 had a positive but minimal effect on driver behavior at the grade crossings. The MBA successfully demonstrated its value by providing further insight on the safety analysis and by increasing the number of variables that can be analyzed simultaneously. The methodology offers the scientific community an innovative approach to analyzing driver behavior. Practical Applications: The results identified important variables for developing preventive measures, which will ultimately help reduce safety violations at grade crossings. The MBA can provide practical insight for railroad safety professionals and transportation engineers when determining problematic intersections and can be used to improve the education on grade crossing interactions.
In the process of nowadays efficiency evaluation of any mode of transportation, sustainability results are the most important factor. In regard to railway sustainability, Train Control Information Systems (TCIS) are such advanced systems with important positive impacts. The main purpose of this study was therefore the evaluation of these impacts as well as the evaluation of Key Performance Themes (KPT) for sustainable railways. Firstly a very detailed literature review of papers that have focused on TCIS and their improvements on railway sustainability, published in the scientific journal in the period from 2005 and 2016, was performed. The number of studies was then used as a main criteria in Analytical Hierarchical Process (AHP) evaluations or rankings of these systems and their impacts. The paper offers results from the first systematic review of papers which investigate the role of TCIS in terms of sustainability and, additionally, represents a refined approach to TCIS classification with a new classes descriptions. During review KPT for sustainable railways were also identified. Further, AHP evaluated the Train Management and Interlocking Systems and their subsystems as the most important TCIS, and safety and costs of equipment, installation, maintenance and operation as the most important themes. The results are important for both, scholars for their future research and for other railway stakeholders and decision makers, who must select different systems and technologies for implementation in their railway systems with emphasis on increasing performance and sustainability. The study offers also the opportunities for further research in regard to railway sustainability.
Private highway-railroad grade crossings (HRGCs) are intersections of highways and railroads on roadways that are not maintained by a public authority. Since no public authority maintains private HRGCs, fatal and injury crashes at these locations are of concern. However, no study has been conducted at private HRGCs to identify the safety issues that might exist and how to alleviate them. This study identifies the significant predictors of traffic casualties (including both injuries and fatalities) at private HRGCs in the U.S. using six years of nationwide crashes from 2009 to 2014. Two levels of injury severity were considered, injury (including fatalities and injuries) and no injury. The study investigates multiple predictors, e.g., temporal crash characteristics, geometry, railroad, traffic, vehicle, and environment. The study applies both the mixed logit and binary logit models. The mixed logit model was found to outperform the binary logit model. The mixed logit model revealed that drivers who did not stop, railroad equipment that struck highway users, higher train speeds, non-presence of advance warning signs, concrete road surface type, and cloudy weather were associated with an increase in injuries and fatalities. For example, a one-mile-per-hour higher train speed increases the probability of fatality by 22%. On the contrary, male drivers, PM peak periods, and presence of warning devices at both approaches were associated with a fatality reduction. Potential strategies are recommended to alleviate injuries and fatalities at private HRGCs.
Transportation professionals have long recognized the harm of trespassing along railway rights-of-way. However, the non-crossing rail trespassing issue has received less attention compared with highway-rail grade crossing crashes, despite the fact that nearly 8,800 rail-trespassing crashes occurred on non-crossing rail tracks during the past decade with a large number of them resulting in fatality. Also, geographic and socio-demographic diversity within the US implies that trespassing crash severity and its correlates may vary across geographic entities or regions. The purpose of this paper is to investigate these issues using rail-trespasser crash data maintained by Federal Railroad Administration (N=8,794 over 2004 to 2013). The unique aspects of the study are the development of a framework that explores correlates of injury severity in crashes and applies appropriate analysis methods. Specifically, using rigorous spatial modeling methods (Geographically Weighted Regression), the study uncovers spatial variations in correlates between rail-trespassing injury and revealed contributing factors. The factors include personal attributes of individuals, environmental and location attributes, time of crash and pre-crash behaviors. The results show that non-crossing trespass crashes are generally severe with 52.1% involving a fatality. Pre-crash behaviors were found to be key factors showing significant associations with the probability of rail-trespassing injury, especially lying or sleeping (on or near tracks). Fundamentally, the basic assumption of spatial stationarity in traditional regression models does not fully hold in the situation explored. Correlates of injury severity are found to be non-stationary across space. Therefore, regional considerations in specific situations should guide the implementation of treatments and policies.
Solutions to highway-rail grade crossings require understanding driver responses to traffic controls at crossings. This study examines differences in driver behaviors and safety at several types of passive traffic controls at grade crossings utilizing a high-fidelity driving simulator at the University of Tennessee. This paper investigates the use of Stop and Yield signs as viable alternatives to upgrading a passive grade crossing to an active grade crossing. In addition to presenting descriptive statistics, mixed-effects regression models were estimated to handle repeated observations by 64 test subjects. Additionally, path analysis provides a more nuanced interpretation of the results. Stop signs at railroad grade crossings were found to be associated with an increased likelihood of drivers looking for an oncoming train while approaching such passive crossings. They were also more inclined to reduce speeds and or stop. There were fewer violations at crossings with Stop signs when a train coming. The behaviors of drivers when faced with Yield signs were very similar to Crossbuck signs, with little looking or stopping. The findings imply that Stop signs have the potential to decrease the chance of colliding with a train at passive grade crossings and reduce the crash severity even if a crash occurs.
The Moving Ahead for Progress in the 21st Century (MAP-21) includes a separate program that supports safety improvements to reduce the number of fatalities and injuries at public highway-railroad grade crossings (HRGCs). This study identifies the significant factors affecting crash injury severity at public HRGCs in the United States.
Crashes from 2009 through 2013 on 5,528 public HRGCs, extracted from the Federal Railroad Administration database, were used in the analysis. A comprehensive list of risk factors was explored. Examples include predictors related to geographic region of crash, geometry (e.g., area type and pavement marking type), railroad (e.g., warning device type and railroad class), traffic (e.g., train speed and vehicles annual average daily traffic "AADT"), highway user (e.g., driver age and gender), and environment (e.g., lighting and weather conditions). The study used the mixed logit model to better capture the complex highway user behavior at HRGCs.
Female highway users were at higher risk of involvement in injuries and fatalities compared to males. Higher train speeds, very old drivers, open areas, concrete road surface types, and railroad equipment striking highway users before crash, were all found to increase the injury likelihood. On the other hand, young and middle-age drivers, non-passing of standing vehicles at HRGCs, industrial areas, and presence of warning bells were found to reduce injuries and fatalities.
The mixed logit model succeeded in identifying contributing factors of crash severity at public HRGCs and potential countermeasures to reduce both fatalities and injuries are suggested.
It is important to install warning bells at public HRGCs, especially at those with high number of injury and fatality crashes. Enforcement of traffic nearby HRGCs is necessary to prevent vehicles from overtaking of standing vehicles.
Copyright © 2015 Elsevier Ltd. and National Safety Council. Published by Elsevier Ltd. All rights reserved.
A risk-based model is presented for identifying highway-rail grade crossing blackspots. This model consists of two prediction components: collision frequency and collision consequence. A graphic approach is adopted to identify crossings with unacceptable risks (high expected frequencies or consequences or both). These crossings are referred to as blackspots. The model was applied to Canadian inventory and collision occurrence data for the period 1997-2001. Poisson and negative binomial (NB) frequency prediction expressions were developed for crossings with three types of warning devices (signs, flashing lights, and gates). The NB model was found to provide a better fit to the collision frequency data. A weighted consequence score was introduced to represent combined collision severity. The weights used in this combined consequence score were obtained from insurance claims. An NB expression was developed for the collision consequence model. The spatial distribution of blackspots is discussed with respect to the type of warning device, upgrades in warning device, geographic location, and historical collision occurrence. A geographic information system platform was developed for the Ontario region and used to illustrate the spatial pattern of expected and historical collision frequency and associated blackspots.
Many types of statistical models have been proposed for estimating accident risk in transport networks, ranging from basic Poisson and negative binomial models to more complicated models, such as zero-inflated and hierarchical Bayesian models. However, little systematic effort has been devoted to comparing the performance and practical implications of these models and ranking criteria when they are used for identifying hazardous locations. This research investigates the relative performance of three alternative models: the traditional negative binomial model, the heterogeneous negative binomial model, and the Poisson lognormal model. In particular, this work focuses on the impact of the choice of two alternative prior distributions (i.e., gamma versus lognormal) and the effect of allowing variability in the dispersion parameter on the outcome of the analysis. From each model, two alternative accident estimators are computed by using the conditional mean under both marginal and posterior distributions. A sample of Canadian highway-railway intersections with an accident history of 5 years is used to calibrate and evaluate the three alternative models and the two ranking criteria. It is concluded that the choice of model assumptions and ranking criteria can lead to considerably different lists of hazardous locations.
Many types of statistical models have been proposed for estimating accident risk in transport networks, ranging from basic Poisson and negative binomial models to more complicated models, such as zero-inflated and hierarchical Bayesian models. However, little systematic effort has been devoted to comparing the performance and practical implications of these models and ranking criteria when they are used for identifying hazardous locations. This research investigates the relative performance of three alternative models: the traditional negative binomial model, the heterogeneous negative binomial model, and the Poisson lognormal model. In particular, this work focuses on the impact of the choice of two alternative prior distributions (i.e., gamma versus lognormal) and the effect of allowing variability in the dispersion parameter on the outcome of the analysis. From each model, two alternative accident estimators are computed by using the conditional mean under both marginal and posterior distributions. A sample of Canadian highway—railway intersections with an accident history of 5 years is used to calibrate and evaluate the three alternative models and the two ranking criteria. It is concluded that the choice of model assumptions and ranking criteria can lead to considerably different lists of hazardous locations.
The assumptions used by the American Association of State Highway and Transportation Officials (AASHTO) to calculate highway-railroad grade crossing sight distances and documents past research of alternatives to these assumptions are reviewed. The impacts of using alternative assumptions are discussed. A portion of the highway-railroad sight distance table in AASHTOs A Policy of the Geometric Design of Highways and Streets is recalculated.
This feature compares four major classes of warning devices for highway-rail grade crossings. Overall, the analysis shows that the highest collision rates using any basis occurred at locations where the warning device was a stop sign. The push to upgrade crossings from crossbucks only to stop signs may be creating a false sense of improved safety.
Various countermeasures can be introduced to reduce collisions at highway-railway grade crossings. Existing improvements to crossings include the installation of flashing lights or gates, the addition of extra warning devices such as four-quadrant barriers or wayside horns, and the enforcement of speed limits on the approaching highway. Statistical models are needed to ensure that countermeasures introduced at a given crossing are both cost-effective and practicable. However, in large part because of issues of colinearity, poor statistical significance, and parametric bias, many existing statistical models are simple in structure and feature few statistically significant explanatory variables. Accordingly, they fail to reflect the full gamut of factor inputs that explain variation in collision frequency at individual crossings over a given period of time. Before statistical models can be used to investigate the cost-effectiveness of specific countermeasures, models must be developed that more fully reflect the complex relationships that link a specific countermeasure to collision occurrence. This study presents a sequential modeling approach based on data mining and statistical methods to estimate the main and interactive effects of introducing countermeasures at individual grade crossings. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the model in decision support.
Crash reduction factors (CRFs) are used in highway safety studies to predict safety benefits due to reduced numbers of crashes. NCHRP Report 162 identified the need for the development of a national comprehensive set of CRFs for each state to evaluate safety improvements. However, this need has not been met. As a result, many states have developed their own CRFs or have adopted CRFs from other states. A synthesis of the CRF development methods, the associated problems, and the research needs are provided. The emphasis is placed on the before-and-after study method because it has been, and still is, the method of choice for CRF development. Three before-and-after study methods were introduced and reviewed: (a) the simple before-and-after method, (b) the before-and-after study with comparison group method, and (c) the before-and-after study with the empirical Bayes method. The problems associated with the simple before-and-after studies, including regression to the mean, crash migration, maturation, and external causal factor, are discussed. Several research needs related to crash migration and general CRF development are also identified. The information presented in the synthesis will be useful to states that plan to develop or update their CRFs.
Safety levels at highway/rail interfaces continue to be of major concern despite an ever-increasing focus on improved design and appurtenance application practices. Despite the encouraging trend towards improved safety, accident frequencies remain high, many of which result in fatalities. More than half of these accidents occur at public crossings, where active warning devices (i.e. gates, lights, bells, etc.) are in place and functioning properly. This phenomenon speaks directly to the need to re-examine both safety evaluation (i.e. accident prediction) methods and design practices at highway-rail crossings. With respect to earlier developed accident prediction methods, the Peabody Dimmick Formula, the New Hampshire Index and the National Cooperative Highway Research Program (NCHRP) Hazard Index, all lack descriptive capabilities due to their limited number of explanatory variables. Further, each has unique limitations that are detailed in this paper. The US Department of Transportation's (USDOT) Accident Prediction Formula, which is most widely, also has limitations related to the complexity of the three-stage formula and its decline in accident prediction model accuracy over time. This investigation resulted in the development of an alternate highway-rail crossing accident prediction model, using negative binomial regression that shows great promise. The benefit to be gained through the application of this alternate model is (1) a greatly simplified, one-step estimation process; (2) comparable supporting data requirements and (3) interpretation of both the magnitude and direction of the effect of the factors found to significantly influence highway-rail crossing accident frequencies.
Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.
Frequently transportation engineers are required to make difficult safety investment decisions in the face of uncertainty concerning the cost-effectiveness of different countermeasures. For certain types of highway-railway grade crossings, this problem is further aggravated due to the lack of observed before and after collision data that reflects the impact of specific countermeasures. This study proposes a Bayesian data fusion method as an attempt to overcome these challenges. In this framework, we make use of previous research findings on the effectiveness of a given countermeasure, which could vary by jurisdictions and operating conditions to obtain a priori inference on its expected effects. We then use locally calibrated models, which are valid for a specific jurisdiction, to develop the current best estimates regarding the countermeasure effects. By using a Bayesian framework, these two sources are integrated to obtain the posterior distribution of the countermeasure effectiveness. As a result, the outputs provide information not only of the expected collision response to a specific countermeasure but also its variance and corresponding probability distribution for a range of likely values. Examples from Canadian highway-railway grade crossing data are used to illustrate the proposed methodology and the specific effects of prior knowledge and data likelihood on the combined estimates of countermeasure effects.
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