A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing

Compared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategi...

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Main Authors: Jingjing Tian, Hongfei Jia, Guanfeng Wang, Yu Lin, Ruiyi Wu, Ao Lv
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/1905526
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author Jingjing Tian
Hongfei Jia
Guanfeng Wang
Yu Lin
Ruiyi Wu
Ao Lv
author_facet Jingjing Tian
Hongfei Jia
Guanfeng Wang
Yu Lin
Ruiyi Wu
Ao Lv
author_sort Jingjing Tian
collection DOAJ
description Compared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategic planning and operational decision problem separately while ignoring the complicated interaction between them and the distinct features of autonomous vehicles. It is imperative to determine the relocation and pricing strategies at the operational level. In this study, in terms of the pricing strategy, we formalize a logit model to capture the mode choice behavior in a multimodal network, where the reduced VoT is considered simultaneously. A time-space network is employed to capture the daily operation problem based on the elastic demand. The minimum customer service rate is regarded as a constraint to ensure the system’s reliability. Moreover, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly determine the number of stations and parking spaces, fleet size, relocation, and pricing strategies to maximize the total profit. Then, we integrate the Particle Swarm Optimization (PSO) algorithm with the optimization solver Gurobi to address the complex problem. Numerical experiments and comparative analyses are conducted to demonstrate the feasibility and efficiency of the proposed model.
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spelling doaj-art-3a8a9fa013154aa5a312d8ac95fe6fb92025-08-20T02:08:02ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1905526A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and PricingJingjing Tian0Hongfei Jia1Guanfeng Wang2Yu Lin3Ruiyi Wu4Ao Lv5College of TransportationCollege of TransportationCollege of TransportationFaculty of Maritime and TransportationCollege of TransportationCollege of TransportationCompared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategic planning and operational decision problem separately while ignoring the complicated interaction between them and the distinct features of autonomous vehicles. It is imperative to determine the relocation and pricing strategies at the operational level. In this study, in terms of the pricing strategy, we formalize a logit model to capture the mode choice behavior in a multimodal network, where the reduced VoT is considered simultaneously. A time-space network is employed to capture the daily operation problem based on the elastic demand. The minimum customer service rate is regarded as a constraint to ensure the system’s reliability. Moreover, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly determine the number of stations and parking spaces, fleet size, relocation, and pricing strategies to maximize the total profit. Then, we integrate the Particle Swarm Optimization (PSO) algorithm with the optimization solver Gurobi to address the complex problem. Numerical experiments and comparative analyses are conducted to demonstrate the feasibility and efficiency of the proposed model.http://dx.doi.org/10.1155/2022/1905526
spellingShingle Jingjing Tian
Hongfei Jia
Guanfeng Wang
Yu Lin
Ruiyi Wu
Ao Lv
A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
Journal of Advanced Transportation
title A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
title_full A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
title_fullStr A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
title_full_unstemmed A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
title_short A Long-Term Shared Autonomous Vehicle System Design Problem considering Relocation and Pricing
title_sort long term shared autonomous vehicle system design problem considering relocation and pricing
url http://dx.doi.org/10.1155/2022/1905526
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