A Meta-Heuristic Algorithm for Clustering and Monitoring Traffic Streams Using the Integrated Approach of Particle Swarm Optimization Algorithm and Shuhart Control Charts

Traffic monitoring and control in urban and intercity highways plays an effective role in increasing the efficiency of resources and infrastructure, as well as increasing the satisfaction of the stakeholders of transportation systems. Analyzing random traffic variables such as traffic and speed and diagnosing and predicting their unusual situations, which generally indicate the existence of traffic crises, is one of the approaches to monitoring traffic flow and solving traffic problems in metropolises. In this article, by using the data collected by traffic sensors, the data related to traffic variables have been grouped using an innovative algorithm based on the particle swarm optimization method in terms of the traffic situation. The grouped data were used to calculate suitable control limits for time intervals, and the results indicate the high performance of this method. For this purpose, in the first step, using an innovative method based on particle swarm optimization, the training data is grouped based on the values of traffic variables, including density, traffic and speed, and then the appropriate group is identified for each variable value. In the next step, the traffic situation is calculated for the clusters and hours of the day, and based on that, superior and standard control charts are drawn. The obtained results indicate the appropriate accuracy of the proposed system in traffic monitoring.