Using data mining techniques for optimizing traffic signal plans at an urban intersection

A key problem in traffic engineering is the optimization of the flow of vehicles through urban intersections by improving the timing policy of traffic signals. Current methods of signal control policy are based on the junction topography and prespecified static traffic volumes. However, the actual daily traffic volumes can be affected by many time-dependent factors making a static policy hardly optimal. In this paper, we induce nonstationary predictive models of traffic flow by applying novel methods of time-series data mining to the traffic sensors data collected from a signalized intersection in Jerusalem over a period of 3 years. Our methodology for modeling dynamic traffic volumes combines clustering and segmentation algorithms. The results of a case study based on real-world traffic data demonstrate that a dynamic signal policy using the data mining approach can produce a decrease of about 33.7% in the total waiting time of drivers during 1 year, in comparison to the existing static traffic policy. The resulting savings for this junction only would be about 13,800 driving hours, which are worth of about $52,000 per annum in terms of Israeli economy.