Project PI: Dr. Sherif Rabia
Project Team: Dr. Mohamed Hussien, Dr. Amr Yousef, and Eng. Ahmed Tayel
Research area: Intelligent Transportation

Abstract

Road traffic congestion is a chronic problem that has direct and indirect impacts on everyone in Egypt. It wastes time, money, health and even lives. Exposition to different car emissions increases the risk of fatal diseases such as asthma, lung cancer, cardiovascular issues, and premature death. It also increases the emission of greenhouse gases (GHG) that cause global warming and sea-level rise. Furthermore, traffic jams limit the functionality of emergency vehicles, such as ambulances and fire trucks, to respond quickly to their callers, which in turn causes serious threat to people’s lives and to private and public properties.

In this project, it is proposed to use the existing traffic control infra-structure with anticipated low extra costs to build a hybrid model for optimally controlling the switching time of traffic lights and adapt it to different traffic flow capacities. The model is based on supervised learning and computer vision for vehicle detection and traffic flow estimation; and on queuing theory for providing optimal timings of traffic lights that can reduce both the traffic jams and the waiting time at red traffic lights. Additionally, the proposed technique should be able to detect different emergency vehicles and prioritize their crossings in the presence of traffic congestion. Furthermore, it can help in enforcing traffic laws by automatically detecting and reporting traffic violations.