Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding

Vehicular traffic in urban areas faces congestion challenges that negatively impact our lives. The infrastructure associated with intelligent transportation systems provides means for addressing the areas. This study proposes an effective and scalable vehicular avoidance methodology. It introduces a thresholding mechanism to predict avoid during route computation. Our methodology was evaluated validated by employing four road network topologies, three density levels various light configurations, resulting 26 scenarios. Using approach, number of vehicles can run free flow be increased up 200%, whereas scenarios, time spent may reduced 69% CO2 emissions 61%. To best knowledge, prediction domain, this is first approach covers set topologies large representative scenarios simulated testing. Moreover, comparative analysis different other solutions showed we obtained driving emission reduction.