Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges

Since attention to the safety of traffic facilities including freeway interchanges has been increased during recent years, accident prediction models are being developed. Simulation-based surrogate safety measures (SSMs) have been used in the absence of real collision data. But, obtaining different outputs from different SSMs as safety indicators had led to a complexity of using them as the collision avoidance system basis. Additionally, applying SSM requires trajectory data which can be hardly obtained from video processing or calibrated microsimulations. Estimating safety level in different parts of freeway interchanges through a new proposed method was considered in this paper. Fuzzy logic was applied to combine the outputs of different SSMs, and an index called no-collision potential index (NCPI) was defined. 13608 calibrated simulations were conducted on different ramps, weaving, merge, and diverge areas with different geometrical and traffic characteristics, and NCPI was determined for every case. The geometrical and traffic characteristics formed input data of two safety estimator models developed by Artificial Neural Network and Particle Swarm Optimization. Ten freeway interchanges were investigated to calibrate the simulations and to ensure the validity of the fuzzy method and accuracy of the models. Results showed an appropriate and accurate development of the models.

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