Using Mobile Signaling Data to Classify Vehicles on Highways in Real Time

Abstract

Vehicles on the roads have high heterogeneity in vehicle types. Real-time and full-coverage vehicle classification has always been a challenge. Existing intrusive and non-intrusive methods cannot meet the requirements with satisfaction. Considering that signaling data from mobile operators have the advantages such as the wide coverage and the low cost, a new approach named Lepus, which analyzes the signaling stream to achieve the real-time multi-class classification of vehicles on highways, is proposed. Following the Lepus, the historical GPS trajectories with vehicle types and the signaling trajectories occurring at the same time and space are first examined to establish the relation among signaling trajectories, vehicles and vehicle types and then identify signaling-recognizable vehicles. Further, the driving characteristics of these labeled signalingrecognizable vehicles are analyzed so as to determine vehicle classification features. Finally, the vehicle classification model is established and used to analyze the incoming signaling stream and classify the vehicles in real time. Extensive experiments are conducted on real data and the results show that the Lepus approach is effective in real time vehicle classification.