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Data analysis and mining of traffic features based on taxi GPS trajectories: A case study in Beijing
Author(s) -
Liu Chun,
Wang Shuangyan,
Cuomo Salvatore,
Mei Gang
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5332
Subject(s) - rush hour , beijing , traffic congestion , global positioning system , evening , morning , thursday , transport engineering , scheduling (production processes) , weekend effect , computer science , real time computing , geography , telecommunications , engineering , operations management , china , medicine , emergency medicine , linguistics , philosophy , physics , archaeology , astronomy
Summary Taxi GPS trajectories can be mined and used to optimize urban traffic scheduling. The optimization of traffic scheduling is important, especially in megacities such as Beijing. In this paper, we analyze the traffic features in Beijing by mining taxi GPS trajectories. We define the Congestion Coefficient of each edge of the taxi trajectory as the consumed time of a taxi running over a unit of distance. By analyzing the distribution of congestion coefficients of all taxi trajectories, we can observe that, on working days, (1) the congestion coefficient is between 0 and 2 (average speed is greater than 0.5 m/s) and is acceptable to taxi drivers, (2) the morning rush hours are 7:00 ∼ 10:00, (3) the evening rush hours are 17:00 ∼ 20:00, and (4) the traffic congestion in the morning rush hours is worse than that in the evening rush hours; on the weekend, (1) the congestion coefficient is less than 0.2 (average speed is greater than 5 m/s) and is acceptable to taxi drivers; (2) compared with the traffic congestion on working days, there are no significant morning rush hours on the weekend; and (3) the period of the time between 13:00 and 15:00 could be considered the traffic rush hours on the weekend. These findings can be used to improve urban traffic management.