Understanding Citywide Resident Mobility Using Big Data of Electronic Registration Identification of Vehicles

Urban mobility is enjoying much attention due to increasingly serious traffic and environment problems in cities. Private cars are the most important component of urban road traffic. However, current research on urban mobility seldom employs travel data from private cars due to the lack of access to corresponding data acquisition. This problem can be solved with the massive application of Electronic Registration Identification (ERI), which is an emerging technology to identify a unique vehicle based on Radio Frequency Identification (RFID). This paper proposes a framework for discovering the urban mobility of private cars based on ERI data. The main research content includes two parts: trajectory segmentation and attractive area mining. In the trajectory segmentation, stay segments in trajectories are identified by Bayes classification based on the link travel time distribution model. The model parameters of each link are trained by Expectation Maximization(EM) algorithm. In attractive area mining, a spatial clustering algorithm based on data field is introduced. Finally, we utilized real-world data into the proposed algorithms. The experimental results show that the proposed method can accurately segment the trajectory, and the visualization of attractive areas reveals the urban mobility characteristics of private cars.