Developing a Tracking-Based Dynamic Flash Yellow Arrow Strategy for Permissive Left-Turn Vehicles to Improve Pedestrian Safety at Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Recent data have shown that pedestrian fatalities have increased by 44% from 2010 to 2019. In 2019, 6,590 pedestrians died of traffic crashes, and 20% occurred at intersections, the highest in 30 years. These saddening facts, unfortunately, suggest that walking or biking is even less safe today although driving is safer thanks to numerous efforts in vehicular technologies. Smart transportation is successful only if it provides equitable safety for all road users. Flash yellow arrow (FYA) is a left-turn strategy at signalized intersections in North America. It allows left-turn vehicles to cross when the gaps of opposing through-traffic are perceived as safe. But it cannot separate concurrent crossing pedestrians and left-turn vehicles. To address this issue, a novel dynamic flash yellow arrow (D-FYA) solution is developed using the light detection and ranging (LiDAR)-based tracking technique. It can address the safety concerns in the FYA while recovering the permissive left-turn capacity after the concurrent pedestrians are cleared. Depending on the pedestrian volumes, the corresponding FYA with each cycle will either start as scheduled, be postponed, or be canceled within each cycle. The proposed D-FYA was deployed at an intersection next to the campus of the University of Texas at Arlington, and its real-time D-FYA decisions in the field were verified over 100 traffic signal cycles through simultaneous observation in the field. The proposed D-FYA solution was further evaluated within a traffic signal simulation platform to compare its mobility performance with another two permissive left-turn strategies: (1) protected + permissive left turn (PPLT), and (2) PPLT with minus-pedestrian phase. The experiment results revealed the D-FYA is accurate and adaptive compared with the other two permissive left-turn strategies.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. The simulation model for the second case study can be downloaded at https://github.com/pflee2002/D-FYA-VISSIM-Model.
Acknowledgments
This study is a part of a research project titled Using LiDAR sensors to study pedestrian behaviors and safety improvement at signalized intersections, sponsored by the National Institute for Transportation and Communities, a national university transportation center hosted at Portland State University. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They 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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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Mar 29, 2022
Accepted: Nov 18, 2022
Published online: Jan 31, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 30, 2023
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