A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior

XuXun Lin and PengCheng Yuan

Physica A: Statistical Mechanics and its Applications, 2018, vol. 490, issue C, 1096-1110

Abstract: In this research we consider commuters’ dynamic learning effect by modeling the trip mode choice behavior from a new perspective of dynamic evolutionary game theory. We explore the behavior pattern of different types of commuters and study the evolution path and equilibrium properties under different traffic conditions. We further establish a dynamic parking charge optimal control (referred to as DPCOC) model to alter commuters’ trip mode choice while minimizing the total social cost. Numerical tests show. (1) Under fixed parking fee policy, the evolutionary results are completely decided by the travel time and the only method for public transit induction is to increase the parking charge price. (2) Compared with fixed parking fee policy, DPCOC policy proposed in this research has several advantages. Firstly, it can effectively turn the evolutionary path and evolutionary stable strategy to a better situation while minimizing the total social cost. Secondly, it can reduce the sensitivity of trip mode choice behavior to traffic congestion and improve the ability to resist interferences and emergencies. Thirdly, it is able to control the private car proportion to a stable state and make the trip behavior more predictable for the transportation management department. The research results can provide theoretical basis and decision-making references for commuters’ mode choice prediction, dynamic setting of urban parking charge prices and public transit induction.

Keywords: Trip mode; Asymmetric evolutionary game; Parking charge; Dynamic optimal control (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437117307835
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:490:y:2018:i:c:p:1096-1110

DOI: 10.1016/j.physa.2017.08.063

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().