Extending traffic infrastructures to reduce congestion is not always viable and is a very costly solution. The new road may reduce the congestion, but those effects are generally momentary as more drivers use those newly expanded roads. Thus, there is a serious need for a fundamental understanding of the emergence of traffic jam. This paper aims to study the existence of cluster of jams in an ideal traffic flow. The traffic model is developed on a cellular automata platform, with bad driving behaviour such as tailgating is modelled to initiate traffic waves and create a ripple effect that forces the following vehicles to sudden brake when they run into such waves. Congestion state can be determined by clustering the rippling traffic waves. Simulation results show that, the average speed of a particular lane is determined by the spacing between vehicles.


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    Title :

    Clustering of Rippling Traffic Waves for Driving Behavioural Pattern Learning


    Contributors:


    Publication date :

    2018-11-01


    Size :

    313268 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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