In this paper, we studied the relationship between Cell Dwell Time (CDT) and the degree of road traffic congestion. CDT is the duration that a mobile phone remains associated with a base station. As the mobile phone in a vehicle travels along a road and encounters different degrees of congestion, the value of CDT varies accordingly. Intutively, the higher value of CDT indicates the worse degree of congestion. This study investigated ways to estimate the degree of road traffic congestion based on measurements of CDT taken from major traffic routes in the metropolitan area of Bangkok, Thailand. We classifed measurements of CDT into three levels of traffic congestion based on duration using two techniques -simple threshold and fuzzy logic, and verified the results with human observations of traffic conditions. The results suggested that the duration of CDT can be used to estimate the degree of congestion with accuracy level upto 85%, which is comparable to a similar technique used to estimate the degree of congestion based on velocity. This suggested a potential use of CDT as traffic probe data.
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) Kmeans 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.
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