Urban Travel Trajectory Extraction based on WiFi Probe Data

In order to extract the travel trajectory of urban residents more conveniently, analyze the daily spatial behavior of individuals, and provide data support for the decision-making of urban management measures, this paper proposes an urban travel trajectory extraction method based on WiFi probe data, which mainly solves the problem of map matching and lost trajectory reconstruction of WiFi probe data. First, extract the track record sequence by sorting the terminal MAC code and timestamp in multiple columns, and use the RSSI value to extract the candidate point set located on the road network for each record. Secondly, an algorithm based on local evaluation is designed: for each candidate point, the spatio-temporal relationship between the candidate point set extracted from the adjacent records is used to evaluate its temporal consistency and spatial consistency, and then the final score is obtained by combining with the weight function dynamically constructed in inverse time ratio, then the highest score point in each candidate point set is selected as the best matching point. Finally, a depth-first-based path search algorithm is used to search for all feasible paths between the upper and lower points of the lost trajectory, and then the optimal reconstruction path is determined based on the TOPSIS method. In this paper, the WiFi probe data collected in the central area of Dongguan City is used as the experimental data to test, and more than 60 000 tracks can be extracted every day on average. Compared with the GPS data, the feasibility of the method is verified, which provides a new solution for urban travel trajectory mining. 2021, Science Press. All right reserved.