Distributed Algorithm for Traffic Data Collection and Data Quality Analysis Based on Wireless Sensor Networks

The growing need of the real-time traffic data has spurred the deployment of large-scale dedicated monitoring infrastructure systems, which mainly consist of the use of inductive loop detectors. However, the loop sensor data is prone to be noised or even missed under harsh environment. The state-of-the-art wireless sensor networks provide an appealing and low-cost alternative to inductive loops for traffic surveillance. Focusing on the urban traffic data collection, this paper proposes a distributed algorithm to collect the traffic data based on sensor networks and improve the reliability of data by quality analysis. Considering the certain correlated characteristics, this algorithm firstly processes the data samples with an aggregation model based on the mean filter, and then, the data quality is analyzed, and partial bad data are repaired by the cusp catastrophe theory. The performance of this algorithm is analyzed with a number of simulations based on data set obtain in urban roadway, and the comparative results show that this algorithm could obtain the better performance.

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