Hemorheology Based Traffic Congestion and Forecasting Model in the Internet of Vehicles

Abstract

Traffic congestion causes increased vehicular queuing, slower speeds and delay in travel time, continuously claiming many social, economic and environmental problems. While Internet of Vehicles (IoV) advances in equipping vehicles with sensors and actuators that ‘communicates’, classifying and forecasting traffic congestion in real-time and in fast mobility is a sizzling yet challenging research interest. In hemorheology, hypertension can be classified in stages to indicate severity levels, thus a similar analogy need to be tested in traffic to classify congestion levels. This paper attempts to develop a traffic congestion and forecasting model based on hypertension in hemorheology. Traffic congestion was simulated in the city of Shah Alam’s urban area using SUMO urban vehicular mobility simulator. Results show promising and rational adaptation of hemorheology in classifying the severity levels of traffic congestion.