Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in city is major challenge. Particularly, allocation traffic-free route to taxi drivers during peak hours one challenges control flow. So, this paper, between driver and pickup location or hotspot with spatial-temporal dependencies optimized. Initially, hotspots are clustered using density-based spatial clustering applications noise (DBSCAN) algorithm find hot spots at an urban area. Then, optimal allocated pick up customer hotspot. Before allocating route, each spot mapped number drivers. Among map function, selected rain optimization (ROA). If more than function obtained as solution, who has done least trips day chosen final solution This selection leads hours. Evaluation approach depicts that proposed scheme reduces traveling time, waiting fuel consumption, emission.