Modular Autonomous Electric Vehicle scheduling for demand-responsive transit services with modular charging strategy

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics Pub Date : 2025-01-13 DOI:10.1016/j.aei.2025.103114

Yun Yuan, Yitong Li, Xin Li

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引用次数: 0

Abstract

Modular Autonomous Electric Vehicles (MAEV) have shown to provide in-motion transfer and flexible capacity to the demand responsive transit (DRT). However, needs-based charging strategy for the MAEV based DRT systems may reduce the utilization of the MVs during the peak hours. To address this issue, this paper proposes a mixed integer linear programming model for optimizing the route and charging planning of the DRT service, where passenger transfers are assigned to schedule partial charging time between service trips. To deal with the hard problem, an adaptive large neighbourhood search algorithm is developed. A case study regarding the real-world parameters and three numerical testing sets is conducted to show the efficiency and effectiveness of the proposed method. Results show the proposed method has 19.62 %, 12.65 % and 26.81 % reductions on the total system cost in comparison to the MAEV based DRT with the needs-based charging strategy, the comparable system considering transferring at a point, and non-transfer DRT, respectively.

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来源期刊

Advanced Engineering Informatics 工程技术-工程：综合

CiteScore

12.40

自引率

18.20%

发文量

292

审稿时长

45 days

期刊介绍： Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.