A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model’s performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas Fort Worth, Texas, area. The results reveal good accuracy and robustness.
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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 research is supported by the project “Embracing emerging traffic big data (connected vehicle data) in smart city applications to improve transportation systems efficiency, safety, and equity,” sponsored by Center for Transportation Equity, Decisions, and Dollars (CTEDD), a USDOT university research center at the University of Texas at Arlington. It is supported by the University Partnership Program at the North Central Texas Council of Governments (NCTCOG). The connected vehicle data were distributed by Wejo Data Service. The authors also thank Mr. Arash Mirzaei, Dr. Hong Zheng, and Dr. Gopindra Nair of NCTCOG for their suggestions and comments. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the official views or policies of the aforementioned organizations, nor do the contents constitute a standard, specification, or regulation of these organizations.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 18, 2022
Accepted: Nov 16, 2022
Published online: Jan 27, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 27, 2023
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