Assessing rear-end crash potential in urban locations based on vehicle-by- vehicle interactions, geometric characteristics and operational conditions

摘要

Rear-end crashes are one of the most frequently occurring crash types, especially in urban networks. An understanding of the contributing factors and their significant association with rear-end crashes is of practical importance and will help in the development of effective countermeasures. The objective of this study is to assess rear-end crash potential at a microscopic level in an urban environment, by investigating vehicle-by-vehicle interactions. To do so, several traffic parameters at the individual vehicle level have been taken into consideration, for capturing car-following characteristics and vehicle interactions, and to investigate their effect on potential rear-end crashes. In this study rear-end crash potential was estimated based on stopping distance between two consecutive vehicles, and four rear-end crash potential cases were developed. The results indicated that 66.4% of the observations were estimated as rear-end crash potentials. It was also shown that rear-end crash potential was presented when traffic flow and speed standard deviation were higher. Also, locational characteristics such as lane of travel and location in the network were found to affect drivers' car following decisions and additionally, it was shown that speeds were lower and headways higher when Heavy Goods Vehicles lead. Finally, a model-based behavioral analysis based on Multinomial Logit regression was conducted to systematically identify the statistically significant variables in explaining rear-end risk potential. The modeling results highlighted the significance of the explanatory variables associated with rear-end crash potential, however it was shown that their effect varied among different model configurations. The outcome of the results can be of significant value for several purposes, such as real-time monitoring of risk potential, allocating enforcement units in urban networks and designing targeted proactive safety policies.