Optimizing Customized Bus Lines Considering Users' Transfer Willingness under Cooperative and Competitive Relationship between Metro and Online Car-hailing

IF 5.1 2区工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-08-13 DOI:10.1016/j.tbs.2024.100878

Beibei Wang , Xinyi Qi

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Abstract

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct a questionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

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考虑地铁与网约车合作与竞争关系下用户的换乘意愿，优化定制公交线路

在 "碳峰值 "和 "碳中和 "的背景下，通过多式联运协调个人出行需求、引导旅客选择新型共享公共交通（PT）模式变得越来越重要。本文分析了中国南宁市网约车服务与地铁系统之间的竞争与合作关系，并对不同类型的网约车用户进行了问卷调查。本文构建了一个考虑心理潜变量的混合选择模型，以研究网约车用户对定制公交（CB）的态度和认知。提出了一种改进的自适应基于密度的有噪声应用空间聚类算法（DBSCAN）来识别潜在的拼车站点集，并设计了一种遗传-蚂蚁群落混合算法（GACA）来求解用于优化 CB 线路的双层编程模型。案例研究结果表明，从 OCH 用户到 CB 的总体换乘率为 83.8%，优化方案减少了 69.68% 的碳排放量。

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来源期刊

Travel Behaviour and Society TRANSPORTATION-

CiteScore

9.80

自引率

7.70%

发文量

109

期刊介绍： Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.