Intelligent Traffic Signal Control System Using Machine Learning Techniques

Traffic congestion is a huge problem in almost every developing country as the people using private vehicles are increasing each day and the capacity of the road networks is still not up to the mark. Vehicular traffic problem is very common in urban areas as both private vehicles and other public transportation services are huge in number due to the dense population. This problem affects the functioning of the city. Every individual has to schedule his/her day within the 24 hours time limit. However, traffic volumes in urban areas kill potential time of the individuals. Also, huge amounts of fuel is wasted due to the increasing waiting time, particularly at signal points. Additionally, many urban areas are facing severe air pollution issues. This has very high impact on the health and well-being of the society. To address this issue, we need better and efficient infrastructure of the city and proper management of road traffic. Nowadays, the artificial intelligence (AI) and machine learning (ML) are playing an important role in solving many of the real-world problems. We may use these ML techniques to address road traffic management problem. As the manual maintenance is difficult and not sufficient with the increasing number of vehicles on roads, automation of traffic signal management with ML may result in better traffic conditions in urban areas. The idea is to divide the system into two phases. In the first phase, we classify the traffic signal junctions into one of the three different zones. High-level, medium-level, and low-level traffic zones. Support Vector Machine (SVM) algorithm is used for classification. In second phase, we optimize the signal configuration of high-level traffic zones to bring them to either medium-level or low-level traffic zones.