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A Car-Following Network Model: An Analysis of Trip Delay
https://orcid.org/0000-0003-3522-4357
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 g-forces. 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.
A Car-Following Network Model: An Analysis of Trip Delay
https://orcid.org/0000-0003-3522-4357
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 g-forces. 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.
A Car-Following Network Model: An Analysis of Trip Delay
https://orcid.org/0000-0003-3522-4357
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 g-forces. 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.
A Car-Following Network Model: An Analysis of Trip Delay
J. Transp. Eng., Part A: Systems
Ossenbruggen, Paul J. (author)
2022-09-01
Article (Journal)
Electronic Resource
English
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