Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-32627 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 20 article 901 pages: 29-40

Shehzad Ayaz
National Center for Cloud Computing and Big Data, University of Engineering and Technology Peshawar, Pakistan

Khurram S. Khattak*
National Center for Cloud Computing and Big Data, University of Engineering and Technology Peshawar, Pakistan

Zawar H. Khan
National Center for Cloud Computing and Big Data, University of Engineering and Technology Peshawar, Pakistan

Nasru Minallah
National Center for Cloud Computing and Big Data, University of Engineering and Technology Peshawar, Pakistan

Mushtaq A. Khan
Electrical Engineering Department, University of Engineering and Technology Mardan, Pakistan

Akhtar N. Khan
Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan

Importance of detailed traffic flow characterization is immense for achieving an 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 homogeneous 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 sensor based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision based solution has been proposed for traffic flow characterization under heterogeneous traffic behaviour. The proposed solution was field tested on a complex road configuration, consisting of a two-way multi-lane 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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This work has been funded by the Higher Education Commission, Pakistan to establish a National Center for Big Data and Cloud Computing, University of Engineering and Technology, Peshawar.

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