SENSING TECHNOLOGIES FOR TRAFFIC FLOW CHARACTERIZATION: FROM HETEROGENEOUS TRAFFIC PERSPECTIVE
Abstract
Importance of detailed traffic flow characterization is immense for achieving intelligent transportation system. As such, great efforts in existing literature have gone into proposing different solutions for traffic flow characterization. Among these, first generation intrusive sensors such as pneumatic tube, inductive loop, piezoelectric and magnetic sensors were both labor intensive and expensive to install and maintain. These sensors were able to provide only vehicle count and classification under homogenous traffic conditions. Second generation non-intrusive sensors based solutions, though a marked improvement over intrusive sensors have the capability to only measure vehicle count, speed and classifications. Furthermore, both intrusive and non-intrusive sensors based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision methodology has been employed for traffic characterization under heterogeneous traffic conditions. The proposed solution was field tested on a complex road configuration, comprising of a two-way multilane road with three U-turns. Unlike both intrusive and non-intrusive sensors, the proposed solution can detect pedestrians, two/three wheelers and animal/human driven carts. Furthermore, detailed flow parameters such as vehicle count, speed, spatial/temporal densities, trajectories and heat maps were measured.
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