Ranking and selecting dangerous crash locations: Correcting for the number of passengers and Bayesian ranking plots

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• 作者：Karolien Geurts ; Geert Wets ; Tom Brijs ; Koen Vanhoof and Dimitris Karlis
• 关键词：Ranking and selecting ; Dangerous crash locations ; Hot spots ; Bayesian ranking plots ; Passengers
• 刊名：Journal of Safety Research
• 出版年：2006
• 出版时间：2006
• 年：2006
• 卷：37
• 期：1
• 页码：83-91
• 全文大小：226 K

文摘

Introduction: In this paper a sensitivity analysis is performed to investigate how big the impact would be on the current ranking of crash locations in Flanders (Belgium) when only taking into account the most serious injury per crash instead of all the injured occupants. Results: Results show that this would lead to a different selection of 23.8 % of the 800 sites that are currently considered as dangerous. Conclusions: Considering this impact quantity, the researchers want to sensitize government that giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. Additionally, probability plots are generated to provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Impact on industry Considering the impact quantity of giving weight to the severity of the crash instead of to all the injured occupants of the vehicle on the ranking of crash sites, the authors want to sensitize government to carefully choose the criteria for ranking and selecting crash locations in order to achieve an enduring and successful traffic safety policy. Indeed, giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. However, it is up to the government to decide which priorities should be stressed in the traffic safety policy. Then, the appropriate weighting value combination can be chosen to rank and select the most dangerous crash locations. Additionally, the probability plots proposed in this paper can provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Note that, in practice, one should not only rank the crash locations based on the benefits that can be achieved from tackling these locations. Future research is also needed to incorporate the costs of infrastructure measures and other actions that these crash sites require in order to enhance the safety on these locations. By balancing these costs and benefits against each other, the crash locations can then be ranked according to the order in which they should be prioritized.