Shortest Processing Time Scheduling to Reduce Traffic Congestion in Dense Urban Areas

Traffic congestion is not only a cause of nuisance for general commuters but also a factor that has a measurable impact on the economy if not handled proactively. When congestion increases, the waiting time for commuters increases which results in wasted fuel and wasted time. Wasted fuel adds to the import bill of a country and lost time results in loss of productivity. Traffic can be regulated at various points in order to reduce congestion and eliminate bottleneck areas. In this paper, we propose the use of conventional scheduling to regulate traffic at intersections in order to reduce congestion. We propose minimum destination distance first (MDDF) and minimum average destination distance first (MADDF) algorithms and compare them with some of the relevant existing scheduling algorithms. The proposed algorithms can not only be easily implemented on low cost hardware but also show better performance and outperform the existing algorithms that are considered, based on simulation results. Simulation results show that the MDDF and MADDF algorithms reduce the traffic congestion at intersections by up to 80% in some cases compared to static traffic lights.