Impact of Rainfall and Waterlogging on Traffic Stream Variables in Developing Countries

Adverse weather conditions like fog, snow, and rainfall affect the driver's visibility, road capacity, and vehicles' mobility. The impact of rainfall on the traffic stream variables - traffic flow and speed - is mainly studied for arterial roads of developed countries. These studies use the fine-grain data of traffic and weather collected by Intelligent Transportation Systems (ITS) and weather stations. In developing countries, limited ITS infrastructure is available for traffic monitoring, and the available traffic data are very sparse. The prolonged heavy rain causes massive waterlogging in developing countries due to weak drainage systems, narrow roads, and encroachments.In this paper, we study the impact of rainfall on the traffic stream variables on the arterial, sub-arterial, and collector roads. An empirical model is designed and calibrated for a variety of traffic (peak, off-peak, night) and weather conditions (low, moderate, heavy, prolonged heavy rainfall, and waterlogging). To address the issue of sparse traffic data, we generate synthetic data using the SUMO-1.4.0 traffic simulator. The Krauss car-following model in SUMO is upgraded to use the weather parameters for computing the vehicle speed. The simulation model is validated by comparing the synthetic data with the ground truth data under various traffic and weather conditions. We find that the empirical model accurately captures the effect of rainfall on traffic stream variables, and the synthetic data shows a very good match with the ground truth data.