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Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process
Joint Authors
Cheng, Qixiu
Xing, Jiping
Yi, Wendy
Liu, Zhiyuan
Fu, Xiao
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-06
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Cheng, Qixiu& Xing, Jiping& Yi, Wendy& Liu, Zhiyuan& Fu, Xiao. 2019. Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process. Discrete Dynamics in Nature and Society،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1146538
Modern Language Association (MLA)
Cheng, Qixiu…[et al.]. Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process. Discrete Dynamics in Nature and Society No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1146538
American Medical Association (AMA)
Cheng, Qixiu& Xing, Jiping& Yi, Wendy& Liu, Zhiyuan& Fu, Xiao. Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process. Discrete Dynamics in Nature and Society. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1146538
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1146538