2024
Gu, Xiaoning; Chen, Chao; Feng, Tao; Yao, Baozhen
A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities Journal Article
In: Physica A: Statistical Mechanics and its Applications, vol. 639, pp. 129666, 2024, ISSN: 0378-4371.
Abstract | Links | BibTeX | Tags: Bi-level programming model, Congestion warning, Stackelberg game, Traffic access restriction, Urban traffic control
@article{GU2024129666,
title = {A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities},
author = {Xiaoning Gu and Chao Chen and Tao Feng and Baozhen Yao},
url = {https://www.sciencedirect.com/science/article/pii/S0378437124001754},
doi = {https://doi.org/10.1016/j.physa.2024.129666},
issn = {0378-4371},
year = {2024},
date = {2024-01-01},
journal = {Physica A: Statistical Mechanics and its Applications},
volume = {639},
pages = {129666},
abstract = {Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions.},
keywords = {Bi-level programming model, Congestion warning, Stackelberg game, Traffic access restriction, Urban traffic control},
pubstate = {published},
tppubtype = {article}
}
Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions.
2015
Wang, Y.; Peng, Z.; Wang, K.; Song, X.; Yao, B.; Feng, T.
Research on urban road congestion pricing strategy considering carbon dioxide emissions Journal Article
In: Sustainability (Switzerland), vol. 7, iss. 8, 2015, ISSN: 20711050.
Abstract | Links | BibTeX | Tags: Bi-level programming model, Carbon dioxide emissions, Shuffled frog leaping algorithm (SFLA), Traffic congestion pricing
@article{Wang2015,
title = {Research on urban road congestion pricing strategy considering carbon dioxide emissions},
author = {Y. Wang and Z. Peng and K. Wang and X. Song and B. Yao and T. Feng},
doi = {10.3390/su70810534},
issn = {20711050},
year = {2015},
date = {2015-01-01},
journal = {Sustainability (Switzerland)},
volume = {7},
issue = {8},
abstract = {Congestion pricing strategy has been recognized as an effective countermeasure in the practical field of urban traffic congestion mitigation. In this paper, a bi-level programming model considering carbon dioxide emission is proposed to mitigate traffic congestion and reduce carbon dioxide emissions. The objective function of the upper level model is to minimize the sum of travel costs and the carbon dioxide emissions costs. The lower level is a multi-modal transportation network equilibrium model. To solve the model, the method of successive averages (MSA) and the shuffled frog leaping algorithm (SFLA) are introduced. The proposed method and algorithm are tested through the numerical example. The results show that the proposed congestion pricing strategy can mitigate traffic congestion and reduce carbon emissions effectively.},
keywords = {Bi-level programming model, Carbon dioxide emissions, Shuffled frog leaping algorithm (SFLA), Traffic congestion pricing},
pubstate = {published},
tppubtype = {article}
}
Congestion pricing strategy has been recognized as an effective countermeasure in the practical field of urban traffic congestion mitigation. In this paper, a bi-level programming model considering carbon dioxide emission is proposed to mitigate traffic congestion and reduce carbon dioxide emissions. The objective function of the upper level model is to minimize the sum of travel costs and the carbon dioxide emissions costs. The lower level is a multi-modal transportation network equilibrium model. To solve the model, the method of successive averages (MSA) and the shuffled frog leaping algorithm (SFLA) are introduced. The proposed method and algorithm are tested through the numerical example. The results show that the proposed congestion pricing strategy can mitigate traffic congestion and reduce carbon emissions effectively.
2006
Yang, Z. -Z.; Miao, G. -Q.; Feng, T.
Forecast on maximum car ownership with constraint of environmental capacity in urban Journal Article
In: Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, vol. 19, iss. 6, 2006, ISSN: 10017372.
Abstract | BibTeX | Tags: Bi-level programming model, Car ownership, Sensitivity analysis, Traffic engineering, Traffic environmental capacity, Traffic volume assignment
@article{Yang2006,
title = {Forecast on maximum car ownership with constraint of environmental capacity in urban},
author = {Z. -Z. Yang and G. -Q. Miao and T. Feng},
issn = {10017372},
year = {2006},
date = {2006-01-01},
journal = {Zhongguo Gonglu Xuebao/China Journal of Highway and Transport},
volume = {19},
issue = {6},
abstract = {A model which is a bi-level optimal problem and can forecast the maximum car ownership in urban with constraint of urban environmental capacity was developed. The upper level maximizes the car ownership subjected to the environmental capacity constraint, where the objective function is the maximum of the sum of zonal car population and constraints are environmental capacities on all links. The lower level assigns the OD traffic on road network with user equilibrium method, which simulates the behaviors of traveller path selection and forecasts distribution and running characteristics of traffic demand on road network. In order to realize the feedback between the two levels and solve two optimization problem simultaneously, an algorithm based on sensitivity analysis was developed and a numerical test was used to verify the effectiveness of model and algorithm.},
keywords = {Bi-level programming model, Car ownership, Sensitivity analysis, Traffic engineering, Traffic environmental capacity, Traffic volume assignment},
pubstate = {published},
tppubtype = {article}
}
A model which is a bi-level optimal problem and can forecast the maximum car ownership in urban with constraint of urban environmental capacity was developed. The upper level maximizes the car ownership subjected to the environmental capacity constraint, where the objective function is the maximum of the sum of zonal car population and constraints are environmental capacities on all links. The lower level assigns the OD traffic on road network with user equilibrium method, which simulates the behaviors of traveller path selection and forecasts distribution and running characteristics of traffic demand on road network. In order to realize the feedback between the two levels and solve two optimization problem simultaneously, an algorithm based on sensitivity analysis was developed and a numerical test was used to verify the effectiveness of model and algorithm.