Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data
Publication: Journal of Urban Planning and Development
Volume 148, Issue 2
Abstract
In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers’ clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles’ abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle’s actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations.
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Acknowledgments
This research was partially supported by the project “Exploring a Novel Public–Private–Partnership Data Sharing Policy through a collaborative Arterial Traffic Management System” sponsored by the USDOT UTC Center, Center for Transportation Equity, Decisions, and Dollars (CTEDD). The proposed map-matching method was inspired by the discussion with Dr. Simon Zhou from Arizona State University. 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 above organizations, nor do the contents constitute a standard, specification, or regulation of these organizations.
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Journal of Urban Planning and Development
Volume 148 • Issue 2 • June 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: May 18, 2021
Accepted: Dec 21, 2021
Published online: Feb 22, 2022
Published in print: Jun 1, 2022
Discussion open until: Jul 22, 2022
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