An Extended Multilane Lattice Hydrodynamic Model Considering the Predictive Effect of Drivers under Connected Vehicle Environment
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 10
Abstract
As the main road pattern, multilane roads are prevalent on high-grade highways. In a connected vehicle environment, drivers can perceive the full view of traffic information on each lane, which provides more opportunities for flexibly changing lanes on multilane highways. In addition, drivers can predict the traffic status on short notice and regulate the vehicle’s operating state in advance. This study determined the predictive effect of drivers in a multilane scenario using the lattice hydrodynamic model. The stability criteria of the proposed model were deduced via the reductive perturbation method; when the stability conditions do not hold, the modified Korteweg–de Vries (mKdV) equation can be deduced. By solving this above equation, we derived the kink–antikink soliton wave solution, which can be used to analyze and explain the formation and evolution process of traffic jams. The results show that the number of lanes and the prediction time of drivers considerably affect the stability of traffic flow. Simulation examples verified that when the predictive time is fixed and the number of lanes increases from 1 to 4, the fluctuation amplitude of traffic density decreases from 0.2 to 0.08 even with exogenous initial disturbance; when the number of lanes is fixed, the density fluctuation amplitude decreases as the predictive time increases; and when the predictive time increases to 0.7, the fluctuation amplitude of traffic density approximates to 0 and is a uniform flow.
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Data Availability Statement
Some or all data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is jointly supported by the Guangdong Basic and Applied Research Foundation (Project Nos. 2022A1515010948, 2019A1515111200, 2019A1515110837, and 2023A1515011696), and the National Science Foundation of China (Project Nos. 72071079 and 52272310).
Author contributions: Cong Zhai: conceptualization, methodology, funding acquisition, validation, formal analysis, investigation, and writing—review and editing; Weitiao Wu: conceptualization, methodology, formal analysis, writing—review and editing, and funding acquisition; and Yingping Xiao: writing—review and editing, and funding acquisition.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 8, 2022
Accepted: May 23, 2023
Published online: Jul 31, 2023
Published in print: Oct 1, 2023
Discussion open until: Dec 31, 2023
ASCE Technical Topics:
- Driver behavior
- Engineering fundamentals
- Fluid dynamics
- Fluid mechanics
- Highway and road management
- Highway transportation
- Highways and roads
- Hydrodynamics
- Hydrologic engineering
- Hydrologic models
- Infrastructure
- Lattices
- Models (by type)
- Structural engineering
- Structural systems
- Traffic engineering
- Traffic flow
- Traffic management
- Traffic models
- Transportation engineering
- Vehicles
- Water and water resources
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Cited by
- Cong Zhai, Kening Li, Ronghui Zhang, Tao Peng, Changfu Zong, Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment, Chaos, Solitons & Fractals, 10.1016/j.chaos.2024.114791, 182, (114791), (2024).
- Sunita Yadav, Poonam Redhu, Analysis of passing behavior on car-following model under the influence of cyberattacks, Nonlinear Dynamics, 10.1007/s11071-024-09348-7, 112, 9, (7269-7289), (2024).