Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process
This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequi...
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Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2019/7438147 |
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doaj-f7b253eb5cfd4690930f0be9695853fe2020-11-24T21:56:08ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2019-01-01201910.1155/2019/74381477438147Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution ProcessQixiu Cheng0Jiping Xing1Wendy Yi2Zhiyuan Liu3Xiao Fu4Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, ChinaJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, ChinaSchool of Engineering and Advanced Technology, College of Sciences, Massey University, Auckland, New ZealandJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, ChinaSchool of Transportation, Southeast University, ChinaThis paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model.http://dx.doi.org/10.1155/2019/7438147 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qixiu Cheng Jiping Xing Wendy Yi Zhiyuan Liu Xiao Fu |
spellingShingle |
Qixiu Cheng Jiping Xing Wendy Yi Zhiyuan Liu Xiao Fu Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process Discrete Dynamics in Nature and Society |
author_facet |
Qixiu Cheng Jiping Xing Wendy Yi Zhiyuan Liu Xiao Fu |
author_sort |
Qixiu Cheng |
title |
Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process |
title_short |
Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process |
title_full |
Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process |
title_fullStr |
Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process |
title_full_unstemmed |
Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process |
title_sort |
distance-based congestion pricing with day-to-day dynamic traffic flow evolution process |
publisher |
Hindawi Limited |
series |
Discrete Dynamics in Nature and Society |
issn |
1026-0226 1607-887X |
publishDate |
2019-01-01 |
description |
This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model. |
url |
http://dx.doi.org/10.1155/2019/7438147 |
work_keys_str_mv |
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1725859375832104960 |