Highway Railroad Grade Crossings Research Articles

Analysis of performance of transitional preemption strategy for traffic signal near at-grade railway grade crossing

Because the prime objective of current preemption methods at signalized intersections near highway-railroad grade crossings is to clear the crossing, secondary objectives such as safe pedestrian crossing and vehicle delay are given less consideration or are ignored completely during the preemption. Under certain circumstances state-of-the-practice traffic signal preemption strategies may cause serious pedestrian safety and efficiency problems at signalized intersections near highway-railroad grade crossings. Recently, an Improved Transition Preemption Strategy (ITPS) that is specifically designed to improve intersection performance and pedestrian safety was developed by Cho and Rilett. Even if the ITPS algorithm improved both the safety and efficiency of signalized intersections near highway-railroad grade crossings, it is impossible to measure the exact benefit of ITPS because the ITPS algorithm was tested only for the worst case scenario at the development stage. For this paper, the ITPS algorithm was tested under normal operating condition, that is, trains were designed to arrive at the crossing randomly during the cycle. Also, the effect of pedestrians was analyzed using a VISSIM simulation model which was calibrated to field conditions. Finally, a benefit/cost analysis was performed. It was concluded that the ITPS algorithm improves both the safety and efficiency of signalized intersections near highway-railroad grade crossings for normal operating condition.

Evaluation of Effectiveness of Stop-Sign Treatment at Highway–Railroad Grade Crossings

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.

Evaluation of Effectiveness of Stop Sign Treatments at Highway–Railroad Grade Crossings

The safety benefit of stop sign treatments 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 FRA 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 being controlled only by crossbucks to stop signs, without other traffic control devices 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 only by crossbucks than when they were controlled by stop signs. Further, this study developed negative binomial accident prediction models for paved and unpaved highway–rail grade crossings that included the effect of stop sign treatment. Taking account of specific attributes of crossings controlled only by crossbucks, 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.

Gate Rushing at Highway–Railroad Grade Crossings

This paper deals with the issue of aggressive motor vehicle drivers rushing through closed gates or gates in motion when trains are in close proximity to a highway–railroad grade crossing. The paper's objectives were to report drivers’ frequency of gate rushing and factors associated with it and to investigate drivers’ response to varying degrees of control exercised on their ability to rush through gates. Motor vehicle drivers at a highway–railroad grade crossing were observed by using video equipment before and after a plastic and rubber barrier was installed along the roadway centerline on both sides of the crossing. The installation was in two stages that progressively limited drivers’ ability to go through closed gates. During the first stage, the barriers on both sides of the crossing were installed such that they stopped short of the gates, while in the second stage they were fully extended to the gates. The expectation was that gate rushing will diminish when the barrier is erected but stops short of the gates and that it will further diminish when the barrier is fully extended to the gates. Data analysis showed a gate rush occurring about once every five train crossings; installation of the barrier along the roadway centerline on both sides of the crossing during the first stage reduced gate rushing by 37%. Contrary to the authors’ expectations, no additional reduction in gate rushing was observed when the barriers were fully extended to the gates. Instances of gate rushing increased with longer durations of road closures but decreased if crossing trains stopped on the tracks or if weather conditions were not favorable.

Applying a Sociotechnical Framework for Improving Safety at Highway-Railroad Grade Crossings

To understand drivers' decisions and actions at grade crossings, we examined human factors contributing to noncompliance at grade crossings using a sociotechnical framework. This perspective allows driver behavior at grade crossings to be examined not as individual elements but rather as a function of how each element interacts with other elements within the system. In this paper, we present a model that addresses driver decision-making at grade crossings at a systems level. We identify and describe four elements of the grade crossing system influencing driver compliance: the design of the grade crossing environment, driver characteristics, the role of organizations and management, and social and political forces. We then apply the model to identify how failure to consider safety from a systems perspective contributed to the grade crossing accident that occurred in Fox River Grove, Illinois, in 1995.

Improved Transition Preemption Strategy for Signalized Intersections near At-Grade Railway Grade Crossing

Because the prime objective of the current preemption methods for traffic signals located near highway-railroad grade crossings is to clear vehicles from the crossing, secondary objectives such as maximizing pedestrian safety and minimizing vehicle delay are given less consideration and are often ignored completely. Consequently, the state-of-the-practice traffic signal preemption strategies may result in pedestrian safety and efficiency problems at signalized intersections near highway-railroad grade crossings (IHRGCs). The state-of-the-art transition preemption strategy (TPS) algorithm was designed to reduce the probability that pedestrians would be endangered when the standard preemption was initiated by approaching trains. However, there is a possibility of failure because the algorithm is predicated on the assumption that train arrivals can be predicted with certainty. Given the fact that current architecture may not be adequate with respect to safety and efficiency, there is a definite need for research on how to improve traffic signal preemption strategies. In this paper, an improved transition preemption strategy (ITPS) that is specifically designed to improve intersection performance while maintaining or improving the current level of safety is developed. It was concluded that the ITPS algorithm improves the operation of IHRGCs with respect to control delay while simultaneously reducing the probability of pedestrian conflicts associated with the standard preemption method and the TPS algorithm.

Evaluation of Flexible Traffic Separators at Highway–Railroad Grade Crossings

Flexible traffic separator systems, widely used to alert motorists to lane restrictions, have been suggested as relatively inexpensive, easily maintainable countermeasures to discourage motorists from driving around the gates at highway-railroad grade crossings. A flexible traffic separator system was installed and evaluated at three crossings in central Florida using a before and after study. The effectiveness of the separators was determined by the number of vehicles driving around the gates, compared with the number of vehicles having a chance to do so. The effect was significant, in that a total of 25 out of 2,194 vehicles drove around the gates before the separator installation, whereas only one out of 1,246 vehicles was involved in this type of violation after the treatment. The presence of the traffic separators was not shown to have a significant effect on crossing actions of approaching motorists when the gates were descending. There was, however, some evidence that drivers were more inclined to enter the crossing when the gates were ascending after the train passage.

Bayesian multiple testing procedures for hotspot identification

Ranking a group of candidate sites and selecting from it the high-risk locations or hotspots for detailed engineering study and countermeasure evaluation is the first step in a transport safety improvement program. Past studies have however mainly focused on the task of applying appropriate methods for ranking locations, with few focusing on the issue of how to define selection methods or threshold rules for hotspot identification. The primary goal of this paper is to introduce a multiple testing-based approach to the problem of selecting hotspots. Following the recent developments in the literature, two testing procedures are studied under a Bayesian framework: Bayesian test with weights (BTW) and a Bayesian test controlling for the posterior false discovery rate (FDR) or false negative rate (FNR). The hypotheses tests are implemented on the basis of two random effect or Bayesian models, namely, the hierarchical Poisson/Gamma or Negative Binomial model and the hierarchical Poisson/Lognormal model. A dataset of highway–railway grade crossings is used as an application example to illustrate the proposed procedures incorporating both the posterior distribution of accident frequency and the posterior distribution of ranks. Results on the effects of various decision parameters used in hotspot identification procedures are discussed.

Reducing Undesirable Actions of Motor Vehicle Drivers at Railroad–Highway Grade Crossings

This paper reports on unsafe actions of motor vehicle drivers at gated railroad–highway grade crossings and behavioral modifications in response to installation of a centerline barrier that prevented drivers from going through the gates. A comparison of driver behavior in the pre- and postbarrier periods indicated the effectiveness of the barrier in reducing unsafe actions. A major concern of the Nebraska Department of Roads is the potential for crashes at railroad–highway grade crossings that result from unsafe actions of motorists such as rushing the gates to beat an oncoming train and playing “chicken” with an approaching train (i.e., intentionally standing a motor vehicle on the tracks and moving only when the train brakes). Some driver actions at crossings (e.g., backing up, U-turns) may result in crashes involving motor vehicles only. The railroad crossing at North 141st Street in Waverly, Nebraska, was studied for about 4 months with the help of a night vision camera and a digital video recorder. Motor vehicle driver behavior was observed when the gates were down, and unsafe actions were noted. To reduce gate rushing and other unsafe behaviors, a flexible rubber and plastic barrier was installed to prevent motorists from going through the gates. The postbarrier actions of motor vehicle drivers were monitored and compared with the preinstallation period by using appropriate statistical tools. The results provide interesting insights into how drivers modify their actions in response to physical changes at railroad crossings, in this case installation of a barrier along the street centerline. The barrier reduced some of the more dangerous types of driver actions such as gate rushes.

Estimating countermeasure effects for reducing collisions at highway–railway grade crossings

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.

A Comparative Study of Alternative Model Structures and Criteria for Ranking Locations for Safety Improvements

In transportation safety literature, many statistical models and criteria have been proposed for quantifying risk at transportation facilities such as roadway intersections and highway-rail grade crossings, and identifying candidate locations, or blackspots, for engineering improvements. There are, however, few systematic studies on the comparative performance and practical implications of these models and criteria. The primary goal of this study is to investigate the relative impacts that the use of various alternative models and ranking criteria can have on identifying blackspots. Three alternative models are considered in this investigation, including the popular negative binomial model (NB), the heteroge- neous negative binomial model (HNB), and the zero inflated negative binomial model (ZINB). The expected accident frequency based on both marginal distribution and posterior distribution is considered as a ranking criterion. A sample of highway-railway grade crossings located in the Canadian railway network is used in this investigation as an application environment.

Modeling Signalized Intersections near Highway–Railroad Grade Crossings

Many roadway intersections throughout North America are located near highway–railroad grade crossings (IHRGCs). Numerous safety, operational, and legal challenges are associated with IHRGCs; and these become significantly more complex when traffic signals are present. Because of the complexity of traffic operations at IHRGCs, existing macroscopic analysis approaches are not suitable for in-depth analyses. Consequently, a more detailed methodology for the analysis of intersections with traffic signals located near IHRGCs was developed. The approach is microsimulation based and relies on hardware-in-the-loop architecture to model the traffic signal controller. To demonstrate the methodology, sensitivity analyses of key design parameters were undertaken, and their impacts on safety and delay were analyzed. Specifically, ( a) the maximum train speed and the corresponding length of the detector, ( b) the effect of the pedestrian volume, and ( c) the effect of the addition of an additional upstream train detector were examined in terms of safety and delay at the IHRGC. The methodology was tested with empirical data from a test bed in College Station, Texas. It was found that the manner in which the maximum train speed was calculated could have a statistically significant effect on delay and a detrimental effect on safety. It was also shown that the addition of an additional upstream detector could reduce delay and increase safety.

Modeling Signalized Intersections near Highway-Railroad Grade Crossings: Sensitivity Analyses of Key Design Parameters

Many roadway intersections throughout North America are located near highway-railroad grade crossings (IHRGCs). Numerous safety, operational, and legal challenges are associated with IHRGCs; and these become significantly more complex when traffic signals are present. Because of the complexity of traffic operations at IHRGCs, existing macroscopic analysis approaches are not suitable for in-depth analyses. Consequently, a more detailed methodology for the analysis of intersections with traffic signals located near IHRGCs was developed. The approach is microsimulation based and relies on hardware-in-the-loop architecture to model the traffic signal controller. To demonstrate the methodology, sensitivity analyses of key design parameters were undertaken, and their impacts on safety and delay were analyzed. Specifically, (a) the maximum train speed and the corresponding length of the detector, (b) the effect of the pedestrian volume, and (c) the effect of the addition of an additional upstream train detector ...

A structured model for evaluating countermeasures at highway–railway grade crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

A Model for Evaluating Countermeasures at Highway–Railway Grade Crossings

Current collision prediction models fail to account for the full spectrum of relevant factors affecting the number of collisions at specific highway– railway grade crossings. A number of reasons contribute to this failure, including biases in model parameters resulting from collinearity in the model inputs, absence of important variables in the prediction model caused by lack of statistical significance, the inability of models to consider higher-order interactions, and the presence of unexplained variation in the prediction estimates. These problems have compromised the use of collision prediction models in decisions concerning the development and evaluation of cost-effective safety treatments or countermeasures for application at specific crossings. This paper introduces a stratified collision prediction model for highway–railway grade crossings. The development of this model involves three steps: ( a) crossing inventory variables are expressed in terms of a limited number of orthogonal (nonlinear) underlying attributes or factors; ( b) factor scores are estimated for each crossing and factor, and these scores are used as “seed points” in a subsequent clustering exercise to yield groups or clusters of crossings with similar underlying attributes; and ( c) for each cluster, separate collision prediction models are developed and include important treatment input variables of interest to decision makers and planners. The paper describes an application of a stratified collision prediction model to Canadian highway–railway grade crossing inventory and collision occurrence data for the period 1993 to 2001. The usefulness of the model in estimating collision reduction benefits of selected treatments is illustrated with reference to two countermeasure strategies: upgrades in the type of warning device and the removal of whistle prohibition.

Evaluating Factors Affecting Safety at Highway–Railway Grade Crossings

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.

A Model for Evaluating Countermeasures at Highway-Railway Grade Crossings

Current collision prediction models fail to account for the full spectrum of relevant factors affecting the number of collisions at specific Highway-Railway grade crossings. A number of reasons contribute to this failure, including biases in model parameters resulting from collinearity in the model inputs, absence of important variables in the prediction model caused by lack of statistical significance, the inability of models to consider higher-order interactions, and the presence of unexplained variation in the prediction estimates. These problems have compromised the use of collision prediction models in decisions concerning the development and evaluation of cost-effective Safety treatments or counter-measures for application at specific crossings. This paper introduces a stratified collision prediction model for Highway-Railway grade crossings. The development of this model involves three steps: (a) crossing inventory variables are expressed in terms of a limited number of orthogonal (nonlinear) under...

Evaluating Factors Affecting Safety at Highway-Railway Grade Crossings

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.

Collision Frequency Analysis Using Tree-Based Stratification

Although several factors are known to contribute to collisions at highway–railway grade crossings, the mixed effects of the control factors such as a highway class and other countermeasures on collision occurrence are less well explored. This study evaluates the relationship between countermeasures and collision occurrence by using a sequential analytic strategy that combines the tree-based data stratification method with the generalized linear regression technique. After the control factor effects were controlled with the use of the tree-based data partitioning method, stratified collision prediction models were developed, and the adjusted effect of the selected countermeasures was identified. The conventional statistical models were also provided for comparison with stratified models. The study used Canadian inventory and collision data to demonstrate the expected collision reductions resulting from changes in selected countermeasures, such as warning devices and posted speed limit.

Collision Frequency Analysis Using Tree-Based Stratification

Although several factors are known to contribute to collisions at highway-railway grade crossings, the mixed effects of the control factors such as a highway class and other countermeasures on collision occurrence are less well explored. This study evaluates the relationship between countermeasures and collision occurrence by using a sequential analytic strategy that combines the tree-based data stratification method with the generalized linear regression technique. After the control factor effects were controlled with the use of the tree-based data partitioning method, stratified collision prediction models were developed, and the adjusted effect of the selected countermeasures was identified. The conventional statistical models were also provided for comparison with stratified models. The study used Canadian inventory and collision data to demonstrate the expected collision reductions resulting from changes in selected countermeasures, such as warning devices and posted speed limit.