Abstract
While high road safety performing countries base their effective strategies on reliable data, in developing countries the unavailability of essential information makes this task challenging. As a result, this drawback has led researchers and planners to face dilemmas of “doing nothing” or “doing ill”, therefore restricting models to data availability, often limited to socio-economic and demographic variables. Taking this into account, this study aims to demonstrate the potential improvements in spatial crash prediction model performance by enhancing the explanatory variables and modelling casualties as a function of a more comprehensive dataset, especially with an appropriate exposure variable. This includes experimental work, where models based on available information from São Paulo, Brazil, and Flanders, the Dutch speaking area of Belgium, are developed and compared with each other. Prediction models are developed within the framework of Geographically Weighted Regression with the Poisson distribution of errors. Moreover, casualties and fatalities as the response variables in the models developed for Flanders and São Paulo, respectively, are divided into two sets based on the transport mode, called active (i.e., pedestrians and cyclists) and motorized transport (i.e., motorized vehicle occupants). In order to assess the impacts of the enriched information on model performance, casualties are firstly associated with all available variables for São Paulo and the corresponding ones for Flanders. In the next step, prediction models are developed only for Flanders considering all the available information in the Flemish dataset. Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in AICc and MSPE, respectively. Considering the practical aspects, results could help identify hotspots and relate most influential factors, suggesting sites and data, which should be prioritized in future local investigations. Besides minimizing costs with data collection, it could help policy makers to identify, implement and enforce appropriate countermeasures.
Highlights
Despite efforts to improve road safety, an estimated 1.25 million victims of road crashes worldwide still die every year
This study aims to demonstrate the potential improvements in spatial crash prediction model performance by enhancing the explanatory variables and modelling casualties as a function of a more comprehensive dataset, especially with an appropriate exposure variable
The difficulty in obtaining crash-related information in Brazil, and its consequences in terms of model performance and development of potential studies that could help understand the crash phenomena and enforcement of appropriate countermeasures were the major reasons for carrying out this study
Summary
Despite efforts to improve road safety, an estimated 1.25 million victims of road crashes worldwide still die every year. In Europe, these strategies at both safety-planning and operational levels have led to a steady reduction in the number of deaths, allowing the European fatality rates to decrease far below the global average (9.3 per 100,000 population, relative to the global rate of 17.4) (WHO, 2015) In this context, spatial Crash Prediction Models (CPM) are a critical component in terms of safety planning considering both prediction and impact analysis purposes. In order to highlight the importance of a comprehensive set of explanatory variables within CPM, this study aims to assess the potential impacts of enriched information on model performance, scrutinizing their improvements in terms of statistical and practical contributions This includes empirical work, where models based on crash-related available information from São Paulo, Brazil, and Flanders, the Dutch speaking area of Belgium, are developed and compared. Total number of casualties of motorized transport mode users observed in a TAZ over 3 years
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