Suchergebnisse

Road safety assessment has played a crucial role in the theory and practice of transport management systems. This paper focuses on risk evaluation in the Asian region by exploring the interaction between road safety risk and influencing factors. In the first stage, a data envelopment analysis (DEA) method is applied to calculate and rank the road safety risk levels of Asian countries. In the second stage, a structural equation model (SEM) with latent variables is applied to analyze the interaction between the road safety risk level and the latent variables, measured by six observed performance indicators, i.e., financial impact, institutional framework, infrastructure and mobility, legislation and policy, vehicular road users, and trauma management. Finally, this paper illustrates the applicability of this DEA-SEM approach for road safety performance analysis. ; This research is jointly supported and sponsored by National Natural Science Foundation of China (Grant No. 71701045), TITE and IMOB for publication. Authors would like to thank HE-Boong Kwon (USA), one of the pioneers of a similar method for his valuable guidance.

Objectives: There is a growing trend in development and application of real-time crash risk prediction models within dynamic safety management systems. These real-time crash risk prediction models are constructed by associating crash data with the real-time traffic surveillance data (e.g. collected by loop detectors). The main objective of this paper is to develop a real-time risk model that will potentially be utilized within traffic management systems. This model aims to predict the likelihood of crash occurrence on motorways. Methods: In this study, the potential prediction variables are confined to traffic related characteristics. Given that the dependent variable (i.e. traffic safety condition) is dichotomous (i.e. "no-crash" or "crash"), a rule-based approach is considered for model development. The performance of rule-based classifiers is further compared with the more conventional techniques like binary logistic regression and decision trees. The crash and traffic data used in this study were collected between June 2009 and December 2011 on a part of the E313 motorway in Belgium between Geel-East and Antwerp-East exits, on the direction towards Antwerp. Results: The results of analysis show that several traffic flow characteristics such as traffic volume, average speed and standard deviation of speed at the upstream loop detector station, and the difference in average speed on upstream and downstream loop detector stations significantly contribute to the crash occurrence prediction. The final chosen classifier is able to predict 70% of crash occasions accurately while it correctly predicts 90% of no-crash instances, indicating a 10% false alarm rate. Conclusions: The findings of this study can be used to predict the likelihood of crash occurrence on motorways within dynamic safety management systems. ; This research was carried out within the framework of the Policy Research Centre on Traffic Safety with the support of the Flemish government and was partly supported by a grant from the Research Foundation Flanders (FWO). The content of this article is the sole responsibility of the authors.