Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments

Qingwan Xue, Xuedong Yan, Xiaomeng Li and Yun Wang

Discrete Dynamics in Nature and Society, 2018, vol. 2018, 1-13

Abstract:

Rear-end collisions are one of the most common types of accidents, and the importance of examining rear-end collisions has been demonstrated by numerous accidents analysis researches. Over the past decades, lots of models have been built to describe driving behaviour during car following to better understand the cause of collisions. However, it is necessary to consider individual difference in car-following modelling while it seems to be ignored in most of previous models. In this study, a rear-end collision avoidance behaviour model considering drivers’ individual differences was developed based on a common deceleration pattern extracted from driving behaviour data, which were collected in a car-following driving simulation experiment. Parameters of variables in the model were calibrated by liner regression and Monte Carlo method was adopted in model simulation for uncertainty analysis. Simulation results confirmed the effectiveness of this model by comparing them to the experiment data and the influence of driving speed and headway distance on the rear-end collision risk was indicated as well. The thresholds for driving speed and headway distance were 18 m/s and 15 m, respectively. An obvious increase of collision risk was observed according to the simulation results.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:5861249

DOI: 10.1155/2018/5861249

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