Transferability of Community-Based Collision Prediction Models for Use in Road Safety Planning Applications

2010 ◽  
Vol 136 (10) ◽  
pp. 871-880 ◽  
Author(s):  
Bidoura Khondakar ◽  
Tarek Sayed ◽  
Gord Lovegrove
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Safety Planning ◽  
Community Based ◽  
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.

Combating IPV With Community Leaders in Honduras: An Evaluation of an IPV Training Program Among Teachers and Health Professionals

2021 ◽  
pp. 107780122110089
Author(s):  
Chunrye Kim ◽  
Joel A. Capellan ◽  
Hung-En Sung ◽  
Eduardo Rafael Orellana
Keyword(s):  
Training Program ◽  
Health Care Professionals ◽  
Partner Violence ◽  
Intimate Partner ◽  
Educational Opportunities ◽  
Safety Planning ◽  
Community Members ◽  
Community Based ◽  
Confidence Levels ◽  
The Impact

Intimate partner violence (IPV) among women in Latin America, including Honduras, is serious. To help IPV victims, a community-based educational program has been implemented. This study aims to examine the impact of IPV training among teachers and health care professionals ( n = 160) on increases in IPV knowledge, attitudes, and self-efficacy when dealing with IPV victims using a pretest and posttest design. We found that the treatment group who received IPV training showed significantly lower justification for IPV, higher gender equality attitudes, and higher IPV knowledge as well as higher confidence levels in identifying IPV victims and safety planning for victims. We concluded that the IPV training program using the community-based approaches has the potential to help IPV victims in Honduras. More efforts should be made to increase the educational opportunities the community members can receive.

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.

Sign in / Sign up

Export Citation Format

Share Document