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A Wireless Mesh Network Platform for Vehicle Positioning and Location Tracking
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Author(s): Mohamed EL-Darieby (University of Regina, Canada), Hazem Ahmed (University of Regina, Canada), Mahmoud Halfawy (National Research Council NRC-CSIR, Canada), Ahmed Amer (Zagazig University, Egypt)and Baher Abdulhai (Toronto Intelligent Transportation Systems Centre, Dept. of Civil Engineering, Canada)
Copyright: 2010
Pages: 15
Source title:
Networking and Telecommunications: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60566-986-1.ch040
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Abstract
Large urban areas in North America as well as many other parts of the world are experiencing unprecedented and soaring congestion problems. It is imperative that modern societies upgrade their transportation systems in order to remain competitive, and maintain the high quality of life and social wellbeing. Current practices in Intelligent Transportation Systems (ITS) data gathering are dominated by the use of point detectors for surveillance, and wire-line communication networks for data transmission. Reliance on point detectors is losing appeal due to detector reliability issues, the cost of building and maintaining detector networks, and potential traffic disruption during construction and maintenance of these networks. This chapter describes a novel wireless mesh network platform for traffic monitoring. The platform uses traveling cars as data collection probes and uses wireless municipal mesh networks to transport sensed data. The platform assumes that cars or drivers’ mobile devices are equipped with the widely adopted low-cost Bluetooth wireless technology. Field trials of the proposed platform demonstrated its capability to track cars traveling at speeds of 0 to 70 km/hour. The platform was able to track cars as they travel and turn on a typical road network. In addition, the platform was used to approximate car speeds through determining the change in position in a time period. The preliminary results indicated an accuracy of ± 10%- 15%. The chapter describes the architecture, implementation, and field-testing of the proposed platform. It also discusses aspects of large-scale deployment of the proposed platform to cover large geographic areas.
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