A Car-Following Network Model: An Analysis of Trip Delay
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 9
Abstract
In this study, 20 drivers, who maintain a safe distance at all speeds, are monitored to help determine the principal reason for trip delay. The aim was to develop a driver-assisted technology device or devices to enhance driver comfort and safety. The use of vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or a combination of devices that can quickly collect and analyze data is contemplated. Collecting real-time driver response data is a challenge and extremely expensive. For these reasons, a stochastic car-following model, called a car-following network model (CFNM), was developed. It features 20 simulated passenger-car drivers traveling around a test track with 2 cruising zones and a bottleneck. Each driver strives to minimize the individual trip time. The model accounts for (1) driver decision-making lapses made over time, (2) a driver’s inability to precisely control speed, and (3) a driver’s desire to comfortably accelerate and decelerate. Vehicle acceleration and deceleration rates are constrained for (4) driver comfort, and (5) driver health, i.e., vehicle top and lower speeds are adjusted to limit . A linear acceleration model and stochastic differential equation are introduced into the CFNM. The stochastic contribution associated with a driver’s inability to precisely control speed is called traffic noise. A model calibration scheme was developed that ensures that the CFNM forecasts of time, speed, and location are reliable. The relationships between traffic noise and delay and road design and delay are discussed. Analyses show that road design and traffic platooning, which are closely associated with traffic noise and driver concern for safety, are critical factors. No single factor can explain delay. The forecast results suggest that three separate assisted-driver technologies will be needed to deal with start-up, cruise, and arrival delay.
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Data Availability Statement
The data generated with MATLAB models and code used during the study are available from the author upon request in accordance with data retention policies.
Acknowledgments
The author is indebted to the Syracuse University Collaboration for Unprecedented Success and Excellence (CUSE) Grant Program, a program designed to support faculty and students engaged in interdisciplinary collaborations.
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© 2022 American Society of Civil Engineers.
History
Received: Jul 18, 2021
Accepted: Mar 16, 2022
Published online: Jun 23, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 23, 2022
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- Paul J. Ossenbruggen, An Example of Establishing a Plan to Mitigate Traffic Delay with Microscale Computer Simulated Data, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-7377, 149, 8, (2023).