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Commuting private cars are the most important component of urban road traffic in the morning and the evening rush hours. Identifying commuters from all private cars and mining their travel pattern can provide promising solutions to solve the urban road traffic congestion. However, current research rarely involves private cars due to the difficulty in obtaining the travel data of private cars. Electronic Registration Identification (ERI) based on Radio Frequency Identification (RFID), is an emerging vehicle identification technology to collect travel data of individual vehicles. In this paper, we focus on investigating travel patterns of commuting private cars using ERI big data. A method of identifying commuters from all private cars is presented based on the spatiotemporal similarity measurement. Then regular travel behaviors are mined by spatial and temporal clustering and the individual vehicle with regular travel behaviors is identified as commuting private car. In the experiments, real-world ERI big data from Chongqing is employed into the proposed method. The group of commuting private cars is discovered. We analyzed travel time distribution and hot spot areas of commuting private cars. The results show that the proposed method can accurately identify commuting private cars, and the spatiotemporal characteristics of commuting private cars can reflect commuting time and the residence-workplace relationship. Keywords—ERI data, Private cars, Commuter, Spatiotemporal similarity, Regular travel behavior

Understanding Travel Patterns of Commuting Private Cars using Big data of Electronic Registration Identification of Vehicles