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Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

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3 Author(s)
W. Hongsakham ; Department of Computer Science, Thammasat University, National Science and Technology Development Agency (NSTDA), Klong Luang, PATHUMTHANI 12120 THAILAND ; W. Pattara-atikom ; R. Peachavanish

This research proposes alternative methods for estimating degrees of road traffic congestion by using cell dwell time (CDT) information available from cellular networks. CDT is the duration that a cellular phone remains associated to a base station between handoff events. As a phone in a vehicle travels along a road having different degrees of congestion, the value of CDT varies accordingly. Measurements of CDT were taken and classified into one of the three degrees of congestion using 1) K-means clustering algorithm and 2) backpropagation neural network. These machine-assigned classifications were then compared against human opinion to assess the accuracy. The results demonstrate the feasibility of using K-means and neural networks in classifying degrees of traffic congestion and that the neural network approach performs well for this task.

Published in:

Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on  (Volume:1 )

Date of Conference:

14-17 May 2008