A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System

Abstract

In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.