Abstract
In order to realize a smart city, it is essential to understand the ever-changing flow of people in the city. Regarding transportation by public transportation, such as trains, bus, and taxis, probe information generated from their operational data of such services are one of the clues, but private cars are operated independently by the citizens and out of such probe data. It is not easy to comprehensively grasp their dynamics because there are also social demands for privacy protection.
In this paper, in order to exceed the limit of covering many mobile objects with probe data alone, a hybrid of fixed-point observation on the environment side and the probe is used to obtain an outline of inbound and outbound to the target area and also transitions of major points in the area. The methodology for grasping dynamics is introduced. Here, cameras are installed at major spots and transportation hubs in the city to identify individual vehicles.
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Distribution of this application ended in 2020.
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Acknowledgments
The author would like to thank Yonezawa City for their cooperation with this research. He is also grateful to Shimane Prefecture, and Matsue National Highway Office of the Ministry of Land, Infrastructure, Transport and Tourism.
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Aihara, K. (2022). Understanding Intra-regional Flow of Vehicles Using Automatic License Plate Recognition. In: Streitz, N.A., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. Smart Environments, Ecosystems, and Cities. HCII 2022. Lecture Notes in Computer Science, vol 13325. Springer, Cham. https://doi.org/10.1007/978-3-031-05463-1_9
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DOI: https://doi.org/10.1007/978-3-031-05463-1_9
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