A simulation study of predicting real-time conflict-prone traffic conditions
journal contributionposted on 01.12.2017, 11:08 by Christos Katrakazas, Mohammed Quddus, Wen-Hua Chen
Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts.
This research was funded by a grant from the UK Engineering and Physical Sciences Research Council (EPSRC) (Grant reference: EP/J011525/1).
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