Traffic Jam Clustering Analysis with Countermeasure Strategies During Traffic Congestion

Countless hours are lost in traffic jam every year. In the efforts to save time, drivers tend to speed up in traffic jam. However there is a traffic paradox suggests that speeding up in traffic jam may not necessarily time saving. Traffic flow is fundamentally dynamic in nature, where the flow formed is greatly subjected to the interaction amongst the drivers. As such, this paper aims to investigate how different speed weightage between the drivers instigating the jam clusters and to assess possible corrective action to reverse jam clusters formation. Based on the identified jam clusters, several properties such as cluster lengths and average speed within the clusters are analysed for corrective action. Traffic simulation with 300 samples on a 5 km length of road shows the proposed algorithm does improve travel time with improvement range from 1.96 % to 7.96 %.