Grid Mapping for Road Network Abstraction and Traffic Congestion Identification Based on Probe Vehicle Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 5
Abstract
Traffic congestion monitoring is a long-term concern in urban areas. However, due to the complex structure of urban road networks and large amounts of traffic data, it is necessary to find an efficient way to identify traffic congestion in urban areas. In the big data era, more and more researchers are using traffic data to model traffic road networks and to identify traffic dynamics. Through the grid mapping method, this paper proposes an efficient abstraction approach to simplify the structure of a road network and then to identify urban traffic congestion. Based on the probe vehicle trajectory data, the intersection nodes between trajectories and grid boundaries are clustered through the method of density-based spatial clustering of applications with noise (DBSCAN). Then, a new traffic performance index is established by the principal component analysis (PCA) method based on the traffic characteristics in the node network. With the case study in Beijing, the proposed method effectively identifies urban traffic congestion in spatial and temporal dimensions. The proposed method is map-independent because it is only based on the probe vehicle data without a digital map. The method is highly efficient for a large urban road network in practice because all the calculations are basic operations based on the cells. Moreover, the proposed method can distinguish the expressway and the frontage roads. The mean absolute error (MAE) is about and the root-mean-square error (RMSE) is lower than . This method is expected to provide valuable spatiotemporal information for traffic engineers and managerial personnel to identify and relieve the traffic congestion problem.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This work is financially supported by Major Projects of Basic Scientific Research Business Expenses (2019JBZ003) of Beijing Jiaotong University. Thanks to the taxi company in Beijing, China for the data provided.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 28, 2020
Accepted: Dec 22, 2020
Published online: Mar 12, 2021
Published in print: May 1, 2021
Discussion open until: Aug 12, 2021
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