Automatic identification of spatio-temporal highway congestion patterns using historic database

Abstract

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s in that the historical dataset provides a pool of spatio-temporally experienced traffic conditions. It is known that traffic flow pattern spatio-temporally recurs and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. This implies that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. That is why the TMCs are reluctant to employ the black-box approaches even though there are numerous valuable research papers published. In this context, this research proposed a more readily understandable and intuitively appealing data-driven approach and developed an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.