Traffic Speed Estimation Using Mobile Phone Location Data Based on Longest Common Subsequence

This paper presents a traffic speed estimation method based on longest common subsequence. The study focuses on user location update mechanism of mobile phone network to reconstruct mobile phone trajectory. The main contribution of this paper is development of a systematic framework for estimating vehicle trajectories from mobile phone location data by longest common subsequence matching algorithm. This framework consists of five steps: vehicle trajectories reconstruction, object road handover sequence generation, vehicle trajectories matching, similarity measurement and traffic speed estimation. First, we analyzed the location update theory to reconstruct vehicle trajectories and generate object road handover sequences. Then, we proposed a vehicle trajectories matching method under the inspiration of longest common subsequence and designed a similarity measurement algorithm that satisfies the specific condition. Moreover, traffic speed can be estimated. At last, in the experiment part, we compared the results between estimated speed in this paper and detected speed by microwave detectors. The estimated speed is consistent with detected speed and has a good precision.