Pedestrian Road Safety Analysis Based on Macro-Level Collision Prediction Models

2021 ◽  
Author(s):  
Zhun Tian ◽  
Shengrui Zhang
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Safety Analysis ◽  
Macro Level ◽  
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.

scholarly journals Making Evidence-based Crash Risk Estimation Routine by using the SESA Process

2020 ◽  
Vol 31 (1) ◽  
pp. 40-50
Keyword(s):  
High Risk ◽  
Road Safety ◽  
High Speed ◽  
Prediction Models ◽  
Risk Estimation ◽  
High Volume ◽  
Safety Analysis ◽  
Crash Risk ◽  
Evidence Based ◽  
Safety Research

Achieving safe system or vision zero outcomes at high-risk urban intersections, especially priority cross-roads and high volume traffic signals, is a major challenge for most cities. Even after decades of crash analysis and improvement works many of these intersections still perform poorly. While best practice for optimising the efficiency of intersections requires the use of modelling tools, like Sidra, this is rarely the case with optimising road safety outcomes. This is despite the large number of evidence-based safety analysis models and tools that are now available to understand intersection crash risk. This paper outlines the SESA (Site-specific Evidence-based Safety Analysis) Process that has been developed to enable transport professionals to estimate and predict crash risk at intersections and other sites. This process utilises existing crash risk estimation tools (based on crash prediction models and crash reduction factors), relevant road safety research, crash severity factors, professional judgement and crash data to predict the underlying crash risk at intersections (and other sites) and the effectiveness of improvement options. The output includes both the number and return period of ‘all injury’ and ‘fatal and serious injury (FSI)’ crashes for each option. The paper includes three applications of the process to high risk intersections in three New Zealand cities, consisting of two priority cross-roads and one high speed roundabout. The case studies demonstrate how the process can be used to assess intersection features and improvement options that are not covered within the available crash estimation tools.

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.

Macro-Level Collision Prediction Models Related to Bicycle Use

ICTIS 2011 ◽  
2011 ◽  
Author(s):  
Feng Wei ◽  
Ahsan Alam ◽  
Gordon Lovegrove
Keyword(s):  
Prediction Models ◽  
Macro Level ◽  
Collision Prediction
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