{"title":"Quantifying the external costs of autonomous on-demand ride pooling services","authors":"","doi":"10.1016/j.cstp.2024.101302","DOIUrl":null,"url":null,"abstract":"<div><div>Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.</div></div>","PeriodicalId":46989,"journal":{"name":"Case Studies on Transport Policy","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies on Transport Policy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213624X24001573","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.