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

2007 ◽  
Vol 2019 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Gordon R. Lovegrove ◽  
Tarek Sayed
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Collision Prediction ◽  
Safety Improvement ◽  
Improvement Programs

Road Safety Resource Allocation Using Interactive Multiobjective Optimization

2021 ◽  
pp. 1-16
Author(s):  
Maria Grazia Augeri
Keyword(s):  
Resource Allocation ◽  
Rough Set ◽  
Road Safety ◽  
Critical Issue ◽  
Allocation Of Resources ◽  
Multi Objective Optimization ◽  
Preference Model ◽  
Integer Variables ◽  
Safety Improvement ◽  
Improvement Programs

A model is proposed for allocating safety resources to various hazard sites. Due to budget constraints, allocation of resources for necessary countermeasures is a critical issue in safety improvement programs. Therefore, the Decision Maker needs a tool that can prioritize the identified countermeasures looking at several objectives, the most important of which are: reducing the number of accidents and minimizing the costs. A number of countermeasures could be implemented simultaneously in the same location and this was considered, so that the solution that best optimizes the objectives was selected. Since the considered objectives are not commensurable, a new methodology with interactive multi-objective optimization in the case of 0-1 integer variables was proposed, based on the application of a logical preference model built using dominance-based Rough Set Approach (IMO-DRSA). Finally, an application of the methodology is presented considering a sample of Italian urban intersections and a set of mutually exclusive alternatives at each location.

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.

Road Safety Improvement Program on Indian Reservations in North Dakota and South Dakota

2015 ◽  
Vol 2531 (1) ◽  
pp. 146-152
Author(s):  
Debbie S. Shinstine ◽  
Khaled Ksaibati
Keyword(s):  
South Dakota ◽  
North Dakota ◽  
Road Safety ◽  
Technical Assistance ◽  
Assistance Program ◽  
Indian Reservations ◽  
Safety Improvement ◽  
Roadway Safety ◽  
Technical Assistance Program ◽  
Improvement Programs

Tribal communities recognize the need to improve roadway safety. A five-step methodology was developed by the Wyoming Technology Transfer Center, Local Technical Assistance Program (WYT2/LTAP), to improve roadway safety on Indian reservations. This methodology was implemented initially on the Wind River Indian Reservation (WRIR); the success of this implementation was the impetus for the Wyoming Department of Transportation, Cheyenne, to fund three systemwide, low-cost safety improvement projects. Given the success of the program on the WRIR, tribes across the country became interested in the program. WYT2/LTAP and the Northern Plains Tribal Technical Assistance Program (NPTTAP) assist tribes to implement this program on their reservations in the Great Plains region and developed criteria to identify tribes to participate. Reservations in North Dakota and South Dakota applied to NPTTAP, and three tribes were accepted to participate: the Standing Rock Sioux Tribe (SRST), the Sisseton Wahpeton Oyate Tribe, and the Yankton Sioux Tribe. Although work had begun on all three reservations, this study focused on the implementation on the roadway safety program by the SRST. Members of the SRST were located in North Dakota and South Dakota, and crash data were collected from each state separately. Because the reporting and years of data differed, several analyses were performed to identify trends in crashes on the SRST. The South Dakota portion of the reservation was compared with statewide rural roads and with the WRIR because the two reservations were of similar size and character. Many challenges and differences were identified through the analysis, which demonstrated that a single procedure would not work for all reservations. Through extensive coordination and collaboration with the tribes and government agencies, WYT2/LTAP and the technical assistance program centers could provide the technical assistance that the tribes would need to develop their own road safety improvement programs.

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.

Using claims prediction model for road safety evaluation

2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed
Keyword(s):  
Prediction Model ◽  
Road Safety ◽  
Claims Data ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Insurance Claims ◽  
Insurance Claim ◽  
Auto Insurance ◽  
Improvement Programs ◽  
Traffic Collision

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.

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