Using Macrolevel Collision Prediction Models in Road Safety Planning Applications

2006 ◽  
Vol 1950 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Gordon R. Lovegrove ◽  
Tarek Sayed
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Safety Planning ◽  
Collision Prediction

Macro-level collision prediction models for evaluating neighbourhood traffic safety

2006 ◽  
Vol 33 (5) ◽  
pp. 609-621 ◽  
Author(s):  
Gordon R Lovegrove ◽  
Tarek Sayed
Keyword(s):  
Linear Regression ◽  
Traffic Safety ◽  
Road Safety ◽  
Prediction Models ◽  
Demand Management ◽  
Macro Level ◽  
Safety Planning ◽  
Transportation Demand ◽  
Regression Modelling ◽  
Collision Prediction

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.

Developing Macrolevel Collision Prediction Models to Evaluate Bicycle Safety in Vancouver, British Columbia, Canada

2017 ◽  
Vol 2659 (1) ◽  
pp. 25-32
Author(s):  
Bianca Popescu ◽  
Tarek Sayed
Keyword(s):  
British Columbia ◽  
Traffic Congestion ◽  
Road Safety ◽  
Prediction Models ◽  
Safety Management ◽  
Safety Issues ◽  
Bicycle Safety ◽  
Collision Prediction ◽  
High Traffic Volume

To encourage greener cities while reducing the impacts of the transportation system—such as impacts on climate change, traffic congestion, and road safety—governments have been investing in sustainable modes of transportation, such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips because cyclists are usually subject to greater safety risks. Engineering approaches to road safety management have traditionally addressed road safety by reacting to existing collision records. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the use of bicycle-related macrolevel (i.e., neighborhood or zonal-level) collision prediction models as empirical tools in road safety diagnosis and planning. These models incorporated an actual bicycle exposure indicator (the number of bicycle kilometers traveled). The macrolevel bicycle–vehicle collisions models were applied at the zonal level to a case study of Vancouver, British Columbia, Canada. Collision-prone zones in Vancouver were identified, and the highest-ranked zones were diagnosed to identify bicycle safety issues and to recommend potential safety countermeasures. The findings from this study suggest that the safety issues may be a result of high density and commercial land use type, coupled with a high traffic volume, particularly on arterial routes, and high bicycle volumes on routes with mixed vehicle and bicycle traffic. The case study demonstrated the use of the models to enhance bicycle safety proactively.

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

A framework to proactively consider road safety within the road planning process

2003 ◽  
Vol 30 (4) ◽  
pp. 711-719 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed
Keyword(s):  
Road Safety ◽  
Planning Process ◽  
Safety Evaluation ◽  
Safety Planning ◽  
The Road ◽  
Existing Problems ◽  
Proactive Approach ◽  
Poor Understanding ◽  
Road Planning

All too often, engineering strategies aimed at improving road safety are reactions to existing problems that occur after a road has been designed and built. Targeting problem locations and developing plans to reduce collisions are vital and have proven to be very successful. Transportation professionals, however, should also take a proactive approach to address road safety before problems emerge. This paper describes an evolving need of how to deal with road safety in a proactive manner. Although a proactive approach should improve the overall safety performance, there is currently a poor understanding of how to proactively plan for road safety. Several logistical and technical obstacles hinder the effective planning for road safety. Each of these obstacles is presented in detail, followed by a description of the opportunity to overcome each obstacle. The paper also includes the results of a case study used to demonstrate the proposed process. A proactive approach to road safety complements traditional, reactive methods currently in use. Significant progress will be realized once safety professionals shift their focus from fixing existing problems to helping plan roads that attempt to be problem free. The net result should be a safer road system.Key words: proactive road safety, safety audits, safety planning, safety evaluation, safety improvements.

scholarly journals Editorial: Advances in Road Safety Planning

2021 ◽  
Vol 3 ◽  
Author(s):  
Krzysztof Goniewicz ◽  
Dorota Lasota
Keyword(s):  
Road Safety ◽  
Safety Planning
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