Modeling Real-Time Car-Following Risk Based on Probability Computation

An innovation feature of this study is modeling real-time car-following risk based on probabilistic concepts. First of all, this paper divided the process of rear-end collision into two stages: first stage is leading vehicle decelerates and the second is the following vehicle can not avoid the collision on the condition of it. On the basis of that, the probability of rear-end collision which used to express the risk of car-following was calculated using the total probability theorem. Whatpsilas more, a deceleration probability density function model was developed which based on the data comes from video detector. In the analysis of the condition on which collision occur while leading vehicle decelerating, brake system response time and coefficient of adhesion were taken into account and this make the model forceful to reflect the real traffic situation. At last, a case study was given using Hang Zhou data, and the data was collect by video detector. The proposed model can be applied to road safety evaluation and intelligent transportation systems.