Collision Prediction Models Research Articles

Macrolevel Collision Prediction Models to Enhance Traditional Reactive Road Safety Improvement Programs

The reactive use is described of 35 recently developed macrolevel collision prediction models (CPMs) to conduct a black spot study with data from 577 urban and rural neighborhoods across Greater Vancouver in British Columbia, Canada. The research objective was to investigate macrolevel CPM use in a traditional reactive safety application (macroreactive use): identification, diagnosis, and remedy of hazardous locations. The results suggested that macroreactive use has the potential to complement traditional road safety improvement programs. Several collision-prone zones were identified and ranked for diagnosis. Two zones were analyzed in detail and revealed several potential enhancements to conventional methods. If adopted for normal use by practitioners, macrolevel CPMs could facilitate improved decisions by community planners and engineers and ultimately could facilitate improved neighborhood road safety for residents and other road users.

Identification of Intersections with Promise for Red Light Camera Safety Improvement

Red light cameras can be used as an alternative tool to supplement police efforts in enforcement against red light running, which is a major contributing factor to vehicle collisions at signalized intersections. The decision to install red light cameras should ensure overall improvement in traffic safety at signalized intersections. The major problem is deciding where and when to install the cameras in order to achieve the safety benefit. Therefore, guidelines are necessary for identifying and priority-ranking those intersections that have promise as sites for potential safety improvement. The primary objective of this study is to develop a safety analysis tool for estimating the safety impact of the installation of red light cameras at signalized intersections. Moreover, this research study provides a tool for identifying and priority-ranking problem intersections with respect to red light running within the entire roadway network under the jurisdiction of a particular agency. The proposed approach uses the empirical Bayes method, collision prediction models, and collision modification factors to estimate the safety changes upon installation of a red light camera at a signalized intersection. The generalized estimating equations technique with the assumption of negative binomial error distribution was used for development of the collision prediction models.

Safety Prediction Models

Urban transportation planning has traditionally focused on capacity and congestion issues with some attention paid to operation and management and with the treatment of such issues typically made proactively. In contrast, road safety has received little attention in the planning process. Safety-conscious planning is a new proactive approach that incorporates safety issues into the transportation planning process. This approach requires a safety planning decision-support tool to facilitate a proactive approach to the assessment of safety implications of alternative network planning initiatives and scenarios. The objective of this research study is to develop a series of zonal-level collision prediction models that are consistent with conventional models commonly used for urban transportation planning. A generalized linear regression modeling approach with the assumption of a negative binomial error structure was employed for exploring relationships between collision frequency in a planning zone and some explanatory variables such as traffic intensity, socioeconomic and demographic factors, land use, and traffic demand measures. Planning-level safety models developed in this study with data for the city of Toronto, Canada, are presented with illustrative applications of how they can be used as decision-support tools for planners to explicitly consider safety in the transportation planning process. Macrolevel collision modification factors are presented to illustrate how the models can be used to examine the impact of each individual planning variable on the safety of an urban zone.

Evaluating the Safety Risk of Narrow Medians Using Reliability Analysis

In British Columbia, many highways are located in mountainous terrain and the costs of highway construction in these areas are high. One method of controlling construction costs is narrow medians but the safety consequences of using narrow medians were never determined. Recent research on safety in geometric design has focused on establishing quantitative relationships between collisions and cross-sectional elements using collision prediction models (CPMs) and collision modification factors (CMFs). In some situations, such as the use of narrow medians, it is difficult to find CPMs and CMFs that adequately describe the design scenario. In other instances, it is difficult to measure the safety in terms of collision reduction because of a lack of data or difficulty isolating the impact of a single design element on collision frequency. In these situations, reliability analysis can be used to evaluate the risk associated with a particular design feature. Reliability analysis is not intended as an alternative to quantify safety using collision frequency but represents a complementary method of measuring safety in terms of risk. In this paper, reliability analysis was completed on a series of horizontal curves with varying horizontal sight distance restrictions and the probability of being unable to stop within the available sight distance was calculated. The results of the study found that narrow medians combined with tight horizontal curves did not provide sufficient sight distance that vehicles would be expected to stop if an object was in a vehicle's path. The analysis was applied to a case study of two constrained alignments in mountainous terrain of British Columbia.

Macro-level collision prediction models for evaluating neighbourhood traffic safety

This study describes the development of macro-level (i.e., neighbourhood or traffic zone level) collision prediction models using data from 577 neighbourhoods across the Greater Vancouver Regional District. The objective is to provide a safety planning decision-support tool that facilitates a proactive approach to community planning which addresses road safety before problems emerge. The models are developed using the generalized linear regression modelling (GLM) technique assuming a negative binomial error structure. The resulting models relate traffic collisions to neighbourhood characteristics such as traffic volume, demographics, network shape, and transportation demand management. Several models are presented for total or severe collisions in rural or urban zones using measured and (or) modelled data. It is hoped that quantifying a predictive traffic safety – neighbourhood planning relationship will facilitate improved decisions by community planners and engineers and, ultimately, facilitate improved neighbourhood traffic safety for residents and other road users.Key words: neighbourhood safety, macro-level collision prediction models, road safety, safety planning, transportation demand management, sociodemographic, generalized linear regression modelling.

Using Macrolevel Collision Prediction Models in Road Safety Planning Applications

This paper describes the use of several recently developed macrolevel collision prediction models (CPMs) in two road safety planning applications. Data from several urban neighborhoods in the Greater Vancouver Regional District (GVRD) in the province of British Columbia, Canada, were used to present and test model-use guidelines in two road safety planning applications. In the first case study, an areawide traffic calming collision modification factor was estimated for GVRD urban areas, for total and severe collisions, to be in the range of -0.40 and -0.39, respectively. In the second case study, four neighborhood network structures were evaluated, and two test networks that appeared safer than conventional grid and cul-de-sac neighborhood street patterns were revealed, mostly because of the increased use of three-way intersections. Moreover, cul-de-sac networks appeared to be much safer than grid networks, by nearly three to one. The results suggested that the models and model-use guidelines could complement and enhance traditional road safety improvement programs and could provide new and improved empirical tools for planners and engineers to do road safety planning. It is hoped that development of guidelines for macrolevel CPM use will facilitate improved decisions by community planners and engineers and, ultimately, neighborhood traffic safety for residents and other road users.

Using Macrolevel Collision Prediction Models in Road Safety Planning Applications

This paper describes the use of several recently developed macrolevel collision prediction models (CPMs) in two road safety planning applications. Data from several urban neighborhoods in the Greater Vancouver Regional District (GVRD) in the province of British Columbia, Canada, were used to present and test model-use guidelines in two road safety planning applications. In the first case study, an areawide traffic calming collision modification factor was estimated for GVRD urban areas, for “total” and “severe” collisions, to be in the range of –0.40 and –0.39, respectively. In the second case study, four neighborhood network structures were evaluated, and two test networks that appeared safer than conventional grid and cul-de-sac neighborhood street patterns were revealed, mostly because of the increased use of three-way intersections. Moreover, cul-de-sac networks appeared to be much safer than grid networks, by nearly three to one. The results suggested that the models and model-use guidelines could complement and enhance traditional road safety improvement programs and could provide new and improved empirical tools for planners and engineers to do road safety planning. It is hoped that development of guidelines for macrolevel CPM use will facilitate improved decisions by community planners and engineers and, ultimately, neighborhood traffic safety for residents and other road users.

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.

Predicting the safety performance associated with highway design decisions: A case study of the Sea to Sky Highway

An important component of any highway design project is the explicit evaluation of safety, leading to the quantification of the safety impacts resulting from changes in highway design parameters and the safety associated with the level of design consistency. Quantification of these safety impacts supports the design process by allowing decision makers the opportunity to analyse the safety benefits in relation to the cost of the highway improvement. This trade-off analysis allows for the justification of highway infrastructure investment. The ability to accurately quantify safety impacts is achieved by using evaluation tools such as collision prediction models, collision modification factors, and measures of design consistency. Although described and available in the safety engineering literature for years, these tools are now becoming widely accepted, since their use responds to the need to quantify safety. This is in sharp contrast to many traditional highway safety assessments, which often relied solely on expert opinion and often failed to adequately support difficult design decisions. Realizing the value of quantifying the safety impacts associated with design decisions, the British Columbia Ministry of Transportation tested an explicit safety evaluation approach for a new design of a section of the scenic Sea to Sky Highway, located between Vancouver and Whistler, in southern British Columbia, Canada. This paper describes the study, illustrating the benefits of this approach for the design process. The paper also offers some improvement over existing safety evaluation techniques.Key words: safety evaluation, collision prediction, collision modification factors, highway design, safety evaluation techniques.

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.

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...

Safety Reviews of Existing Roads: Quantitative Safety Assessment Methodology

Safety reviews of existing roads are becoming an accepted practice in many agencies around the world. These reviews can be highly cost-effective, but the subjective nature of the process can give rise to inconsistencies that limit their effectiveness. To address this issue, a technique to support safety reviews to quantify the safety gains that could be achieved by addressing the problems identified in the review process is presented. The approach is based on known accident relationships. A systematic process to determine which road features should be investigated and how each feature should be evaluated during the review is described. The procedure addresses rural two-lane highways at nonintersections. From the process, a potential for a safety improvement index (PFI) was calculated. Validation of the procedure was carried out by a comparison of the PFI values with the expected collision frequency. PFI was assessed in 406 km of rural two-lane rolling highways in Italy. Collision frequency was determined by application of a collision prediction model, calibrated in the study network, and was refined by application of the empirical Bayes (EB) technique. Correlation between EB safety estimates and PFI values is highly significant, with 93% of the variation in the estimated number of accidents explained by the PFI value. Because of the validation and quantitative nature of the PFI, the procedure can be used to support safety reviews and decision making.

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.

Safety Reviews of Existing Roads

Safety reviews of existing roads are becoming an accepted practice in many agencies around the world. These reviews can be highly cost-effective, but the subjective nature of the process can give rise to inconsistencies that limit their effectiveness. To address this issue, a technique to support safety reviews to quantify the safety gains that could be achieved by addressing the problems identified in the review process is presented. The approach is based on known accident relationships. A systematic process to determine which road features should be investigated and how each feature should be evaluated during the review is described. The procedure addresses rural two-lane highways at nonintersections. From the process, a potential for a safety improvement index (PFI) was calculated. Validation of the procedure was carried out by a comparison of the PFI values with the expected collision frequency. PFI was assessed in 406 km of rural two-lane rolling highways in Italy. Collision frequency was determined by application of a collision prediction model, calibrated in the study network, and was refined by application of the empirical Bayes (EB) technique. Correlation between EB safety estimates and PFI values is highly significant, with 93% of the variation in the estimated number of accidents explained by the PFI value. Because of the validation and quantitative nature of the PFI, the procedure can be used to support safety reviews and decision making.

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.

Pedestrian Collision Prediction Models for Urban Intersections

In more recent safety analysis methods, collision prediction models can be used for identifying intersections with promise of safety improvement and for evaluating the effects of treatment. Considerable effort has been directed at developing collision prediction models, but little has been directed at pedestrian collisions. Collisions involving motor vehicles and pedestrians pose a significant safety problem, principally in urban areas, where the levels of vehicle-pedestrian conflicts are high. Data from Toronto, Canada, are used in the development of pedestrian collision prediction models for three- and four-legged urban intersections, with and without signal control. These models, which relate safety to pedestrian and vehicle traffic volumes, can be used to identify locations that might be targeted for treatment and to help evaluate treatment effects. Models are developed by using pedestrian and vehicular volumes and vehicle volumes only. It is seen that the use of pedestrian volume information results in a much richer model, emphasizing the importance of collecting this information in routine traffic counting programs. An important issue for collision prediction models is transferability to other jurisdictions. This is especially important in the case of pedestrian collision models, because many jurisdictions may not have data sets containing sufficient collisions and pedestrian volume counts with which to calibrate reliable models. Data from the city of Hamilton, Ontario, were used to test the transferability of the Toronto four-legged signalized intersection model. The test was successful: the recalibrated Toronto models predicted collision numbers that were very close to those predicted by the model calibrated directly for the Hamilton data.

Bicycle-associated head injuries and deaths in the United States from 1984 through 1988, how many are preventable? : J.J. Sacks, P. Holmgreen, S.M. Smith, D.M. Sosin, Journal of the American Medical Association, 266(21), 3016–3018.

Today, North American governments are more willing to consider compact neighborhoods with increased use of sustainable transportation modes. Bicycling, one of the most effective modes for short trips with distances less than 5 km is being encouraged. However, as vulnerable road users (VRUs), cyclists are more likely to be injured when involved in collisions. In order to create a safe road environment for them, evaluating cyclists’ road safety at a macro level in a proactive way is necessary. In this paper, different generalized linear regression methods for collision prediction model (CPM) development are reviewed and previous studies on micro-level and macro-level bicycle-related CPMs are summarized. On the basis of insights gained in the exploration stage, this paper also reports on efforts to develop negative binomial models for bicycle–auto collisions at a community-based, macro-level. Data came from the Central Okanagan Regional District (CORD), of British Columbia, Canada. The model results revealed two types of statistical associations between collisions and each explanatory variable: (1) An increase in bicycle–auto collisions is associated with an increase in total lane kilometers (TLKM), bicycle lane kilometers (BLKM), bus stops (BS), traffic signals (SIG), intersection density (INTD), and arterial–local intersection percentage (IALP). (2) A decrease in bicycle collisions was found to be associated with an increase in the number of drive commuters (DRIVE), and in the percentage of drive commuters (DRP). These results support our hypothesis that in North America, with its current low levels of bicycle use (<4%), we can initially expect to see an increase in bicycle collisions as cycle mode share increases. However, as bicycle mode share increases beyond some unknown ‘critical’ level, our hypothesis also predicts a net safety improvement. To test this hypothesis and to further explore the statistical relationships between bicycle mode split and overall road safety, future research needs to pursue further development and application of community-based, macro-level CPMs.

Offshore oil impacts on the bering sea fishing industry

Techniques used in an impact assessment of the effects of offshore petroleum development on the commercial fishing industry in the Bering Sea are presented. Several predictive analytic models were developed to help bound and quantitatively project impacts relating to possible damage and economic loss to the fishing industry. These included models for projecting trawl and crab pot gear loss, increase in vessel collisions and loss in catch due to foreclosure of fishing grounds. Trawl gear loss was predicted by extrapolating the gear loss experience in the North Sea using a model that adjusted the experienced loss rate for differences in the amount of offshore petroleum development and the amount of trawl fishing activity. This model predicted that there would be approximately one instance of gear loss annually for each three trawl vessels fishing the Bering Sea. The theoretical crab pot loss was estimated using a statistical model that estimated the number of pots that would be hit by supply vessel traffic. The results indicated that the theoretical number of pots hit would range from approximately 1.6 to 5 percent of the pots in place. Statistical collision prediction models were used to estimate that the increase in supply vessel traffic would theoretically result in a probability of 0.006 of an additional collision in the year of peak supply vessel activity. A model was developed to estimate loss of catch due to foreclosure of fishing grounds by offshore pipelines, platforms, and other structures. The model indicated that the loss of catch would be less than 5 percent of that calculated by using a method that determined economic loss based on the proportion of the fishing area foreclosed by space usurpation. Assessments regarding effects on the fishing industry labor force and supporting facilities are presented based on a review of analogous situations in other parts of the world. These indicated that adverse impacts on the labor force for fishermen on vessels would be insignificant, but that there would be a certain amount of conflict for fish processing labor and community and support services. The procedures presented here have proved useful in bounding conclusions regarding the magnitude of potential impacts which were previously only dealt with in a subjective manner.