Three Methods of PCU Estimation at Signalized Intersections under Mixed Traffic with UAV Monitoring Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 3
Abstract
Estimation of passenger car unit (PCU) values is very important for traffic capacity analysis and other relevant applications such as determining the saturation flow rate and signal design. However, the usual methods are not completely analogous for mixed traffic due to the presence of a wide variety of vehicle types, poor lane discipline, variability of intraclass vehicles. The parameters required for PCU estimation under mixed traffic are relatively more complex to measure, causing a severe data limitation. The accurate estimation of PCUs relies on the collection of sufficient amounts of data, and unmanned aerial vehicles (UAV) like drones are used in recent years by researchers as they significantly reduce efforts of data collection and extraction. This paper illustrates three methods for PCU estimation under mixed traffic on two signalized intersections of China using UAV monitoring data. The first method is based on the travel time of a vehicle while clearing the intersection. The second method is based on the average speed estimated in terms of different vehicle categories. The lagging headway ratio is used as the basis for PCU estimation in the third method. The author explores the effect of the driving direction and compared the PCU values at different time periods. The results show that the PCU value of vehicles turning left is always greater than that of going straight. The usage of a combined parameter is more appropriate than a single parameter in mixed traffic. The values are similar to those of India but are quite different from the United States. The values obtained in this study can be used as a guideline in the traffic analysis at signalized intersections in China and generally in other developing countries.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the National Natural Science Foundation of China, Grant Nos. 71974043 and 91846301. The authors would like to thank the anonymous reviewers for their valuable comments. The authors declare no conflict of interest.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 3 • March 2022
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© 2021 American Society of Civil Engineers.
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Received: Mar 27, 2021
Accepted: Oct 22, 2021
Published online: Dec 17, 2021
Published in print: Mar 1, 2022
Discussion open until: May 17, 2022
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