考虑到服务类型和地区扩展的共享汽车采用动态,蒙特利尔案例研究的启示

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
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引用次数: 0

摘要

随着时间的推移,汽车共享运营商(CSO)正在调整其服务,以满足不断变化的需求并扩大其市场份额。服务区域不断扩大,在一些城市还出现了 "双模式设置",将自由浮动汽车共享(FFcs)作为一项新服务与现有的基于车站的汽车共享(SBcs)结合起来。本文提出了一种方法来讨论这种情况下的采用动态,特别关注现有服务和服务扩展对采用新服务的影响。我们提出的框架由两部分组成:潜在市场评估和采用模式。潜在市场评估的重点是在特定服务区域内建立当地人口、汽车共享会员和兴趣点(POIs)之间的关系。然后,采用模型描述消费者采用 FFcs 服务的可能性。将这两个模型结合起来,就可以评估服务扩展的效果。我们使用蒙特利尔 Communauto 公司近六年的数据集对该框架进行了评估。前 35 个月的数据被设定为训练数据,而随后的 33 个月则用于验证预测性能。结果表明,所提出的模型能准确预测采用动态。我们发现,SBcs 的先前经验和初始信息传播是需求预测的关键参数,它们决定了早期的采用高峰,并且由于追随者效应,也影响了长期需求。此外,我们还量化了服务区覆盖居民区和兴趣点的重要性,强调了服务区扩展的协同效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Carsharing adoption dynamics considering service type and area expansions with insights from a Montreal case study

Carsharing operators (CSOs) are adapting their service over time to meet changing demands and grow their market share. Service areas are enlarged and, in some cities, “dual-mode settings” evolve, incorporating free-floating carsharing (FFcs) as a new service alongside existing station-based carsharing (SBcs). This paper proposes a methodology to discuss adoption dynamics in such a context, specifically focusing on the impact of existing services and service extensions on the adoption of the new service. We propose a framework, comprising of two parts: a potential market assessment and an adoption model. The potential market assessment focuses on establishing the relationships between the local population, carsharing memberships and Points of Interest (POIs) within the given service area. The adoption model then describes the likelihood of consumers adopting the FFcs service. By combining these two models, the effects of service extensions can be assessed. We evaluate the framework using a nearly six year dataset from Communauto, Montreal. The first 35 months of data are set as training data, while the subsequent 33 months are used for validation of predictive performance. Results demonstrate that the proposed model accurately predicts adoption dynamics. Prior experience of SBcs and initial information spread are found to be key parameters for demand prediction determining early adoption peaks and, due to follower effects, also impact long-term demand. Additionally, we quantify the importance of covering residential areas and points of interests in the service area, highlighting the synergy effects of service area expansions.

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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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