A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City

Abstract

Vehicular pollution becomes a crucial issue within the travel planning of a smart city. Especially, the pollution level at Sensitive Points (SP) like Schools and Hospitals should be kept within a threshold level while a routing solution is offered. In the existing works, the attempt to consider environmental pollution within traffic planning is minimal. In this attempt, we have proposed a framework for offering a routing strategy maintaining the desired pollution level at Sensitive Points. However, the most crucial challenge is to generate an estimation model for measuring pollution at Sensitive Points in an accurate way. In the proposed estimation model, we have attempted to accommodate the meteorological and other essential factors to make it more accurate. The pollution measures as computed by the model within SPs are analyzed for identifying the hot-spots, i.e., the alarming points where the pollution measure is supposed to be higher than the pre-defined threshold. Finally, the rerouting is executed on the affected road segments to maintain the desired level of pollution measured at the hot spots. Moreover, the re-routing has been done (if needed) so that the average remaining travel time of the vehicles will be minimal. Thus, the solution not only focuses on the environmental issues but also addresses the users’ satisfaction in terms of travel time. In the experiment phase, the traffic network is simulated by SUMO, and the entire proposal is implemented to compare with the notable existing comparable works. The proposed approach performs better in terms of the identified metrics, achieving a reduction in Average Vehicle Rerouting (AVR) to 17.26% compared to 20.10% in OPFTCaAP and maintaining a minimal Average Travel Time (ATT) increase for buses (-0.06%).