Road traffic accident prediction for mixed traffic flow using artificial neural network

Transport scenarios in developing countries are fundamentally different from those in developed countries. The latter consists primarily of passenger cars and can be adequately described as “homogeneous” traffic, but the former consists of vehicle types with different static and dynamic characteristics that occupy the same right of way. Vehicle movement is asynchronous. Few studies have attempted to understand the characteristics of mixed traffic. This article explores the sharing attributes and influencing causes of traffic accidents in a mixed traffic area. A predictability model is employed to describe the connection between highway disasters and appropriate constraints such as traffic capacity, road provisions, and atmosphere issues. In this paper, the comparison has been done between the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) predictive models. The study has been conducted at Pimpri Chinchwad Muncipal Corporation (PCMC) region of Pune, Maharashtra, India. For this work, nine years data has been used ranging from the year 2011 to 2019. Results revels that, maximum numbers of accidents were occurred in clear weather condition. Distinctive accidents were occurred due to overloaded vehicle conditions. Also it has been found that the less number of female drivers are responsible for accident. Forecasting model using ANN presents outstanding precision. In this study, additional prominence has been given to the real constraints which are accountable for accident cause in heterogeneous traffic flow.