2024
Wang, Guanfeng; Jia, Hongfei; Feng, Tao; Tian, Jingjing; Wu, Ruiyi; Gao, Heyao; Liu, Chao
In: Physica A: Statistical Mechanics and its Applications, vol. 640, pp. 129667, 2024, ISSN: 0378-4371.
Abstract | Links | BibTeX | Tags: Connected autonomous vehicles, Dual dynamic traffic assignment, Inertial route choice behaviour, Information-sharing, Mixed traffic flow
@article{WANG2024129667,
title = {Modelling the dual dynamic traffic flow evolution with information perception differences between human-driven vehicles and connected autonomous vehicles},
author = {Guanfeng Wang and Hongfei Jia and Tao Feng and Jingjing Tian and Ruiyi Wu and Heyao Gao and Chao Liu},
url = {https://www.sciencedirect.com/science/article/pii/S0378437124001766},
doi = {https://doi.org/10.1016/j.physa.2024.129667},
issn = {0378-4371},
year = {2024},
date = {2024-01-01},
journal = {Physica A: Statistical Mechanics and its Applications},
volume = {640},
pages = {129667},
abstract = {The introduction of connected autonomous vehicles (CAVs) potentially improves the link capacity and backward wave speed of traffic flow, while the advanced communication technology could well make it possible to allow CAV users to share their travel information. To bridge the knowledge gaps in the network evolution under mixed environment of human-driven vehicles (HVs) and CAVs, it is essential to explore multi-dimensional dynamic traffic assignment. An inertia-based multi-class dual dynamic traffic assignment (IMDDTA) model is proposed to capture the intraday and diurnal variations of the mixed traffic flow under the disequilibrium state simultaneously. Specifically, in this study we consider the inertia of HV users as well the information-sharing behaviour of CAV users respectively, characterized by different extensions of the multinomial logit (MNL) model. To demonstrate the properties of the model, two numerical case studies are conducted based on the Braess network and the Sioux Falls network. The results indicate an acceptable validity and applicability of the model and provide valuable insights on the evolution of traffic flow under mixed environment.},
keywords = {Connected autonomous vehicles, Dual dynamic traffic assignment, Inertial route choice behaviour, Information-sharing, Mixed traffic flow},
pubstate = {published},
tppubtype = {article}
}
The introduction of connected autonomous vehicles (CAVs) potentially improves the link capacity and backward wave speed of traffic flow, while the advanced communication technology could well make it possible to allow CAV users to share their travel information. To bridge the knowledge gaps in the network evolution under mixed environment of human-driven vehicles (HVs) and CAVs, it is essential to explore multi-dimensional dynamic traffic assignment. An inertia-based multi-class dual dynamic traffic assignment (IMDDTA) model is proposed to capture the intraday and diurnal variations of the mixed traffic flow under the disequilibrium state simultaneously. Specifically, in this study we consider the inertia of HV users as well the information-sharing behaviour of CAV users respectively, characterized by different extensions of the multinomial logit (MNL) model. To demonstrate the properties of the model, two numerical case studies are conducted based on the Braess network and the Sioux Falls network. The results indicate an acceptable validity and applicability of the model and provide valuable insights on the evolution of traffic flow under mixed environment.
2023
Wang, Guanfeng; Jia, Hongfei; Feng, Tao; Tian, Jingjing; Li, Mengxia; Wang, Luyao
An acceptability-based multi-objective traffic flow adjustment method for environmental sustainability and equity Journal Article
In: Journal of Cleaner Production, vol. 418, pp. 138077, 2023, ISSN: 0959-6526.
Abstract | Links | BibTeX | Tags: Connected autonomous vehicles, Digital product, Emission reduction, Multi-objective bi-level programming, Subsidy nodes deploying scheme, Traffic flow adjustment method
@article{WANG2023138077,
title = {An acceptability-based multi-objective traffic flow adjustment method for environmental sustainability and equity},
author = {Guanfeng Wang and Hongfei Jia and Tao Feng and Jingjing Tian and Mengxia Li and Luyao Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0959652623022357},
doi = {https://doi.org/10.1016/j.jclepro.2023.138077},
issn = {0959-6526},
year = {2023},
date = {2023-01-01},
journal = {Journal of Cleaner Production},
volume = {418},
pages = {138077},
abstract = {As a product of digital development, connected autonomous vehicles (CAVs) offer a unique prospective solution to alleviate the possible performance deterioration of road networks under the mixed environment of human-driven vehicles (HVs) and CAVs. In this paper, we propose a traffic flow adjustment method (TFAM) that treats CAVs as mobile regulators with the purpose to reshape traffic flow distribution on road networks by guiding rather than controlling CAVs. More specifically, we deploy subsidy nodes to briefly outline travel routes and achieve higher acceptability than traditional route-based control schemes. The TFAM is a multi-objective bi-level programming problem where the upper-level problem optimizes the network performance through regulating location and subsidy on the subsidy nodes. The lower-level problem is a dual dynamic traffic assignment (DDTA) model. Apart from the total travel time cost (TTTC), total emission cost (TEC) and network equity (NE) are also introduced as optimization objectives to highlight environmental sustainability and acceptability. To obtain the Pareto solution frontier, a meta-heuristic algorithm with an improved encoding process is proposed. Results of two numerical case studies demonstrate the effects of TFAM on traffic flow distribution and network performance, which yields valuable insights on the optimization of urban traffic systems.},
keywords = {Connected autonomous vehicles, Digital product, Emission reduction, Multi-objective bi-level programming, Subsidy nodes deploying scheme, Traffic flow adjustment method},
pubstate = {published},
tppubtype = {article}
}
As a product of digital development, connected autonomous vehicles (CAVs) offer a unique prospective solution to alleviate the possible performance deterioration of road networks under the mixed environment of human-driven vehicles (HVs) and CAVs. In this paper, we propose a traffic flow adjustment method (TFAM) that treats CAVs as mobile regulators with the purpose to reshape traffic flow distribution on road networks by guiding rather than controlling CAVs. More specifically, we deploy subsidy nodes to briefly outline travel routes and achieve higher acceptability than traditional route-based control schemes. The TFAM is a multi-objective bi-level programming problem where the upper-level problem optimizes the network performance through regulating location and subsidy on the subsidy nodes. The lower-level problem is a dual dynamic traffic assignment (DDTA) model. Apart from the total travel time cost (TTTC), total emission cost (TEC) and network equity (NE) are also introduced as optimization objectives to highlight environmental sustainability and acceptability. To obtain the Pareto solution frontier, a meta-heuristic algorithm with an improved encoding process is proposed. Results of two numerical case studies demonstrate the effects of TFAM on traffic flow distribution and network performance, which yields valuable insights on the optimization of urban traffic systems.