{"title":"您的旅行可共享程度如何?基于路径的搭乘旅行可共享性分析","authors":"Guan Huang , Zhan Zhao , A.G.O. Yeh","doi":"10.1016/j.compenvurbsys.2024.102120","DOIUrl":null,"url":null,"abstract":"<div><p>As an emerging sustainable mobility solution, ridesplitting services match passengers in a similar direction with a single vehicle to reduce fleet size, vehicle kilometers traveled and traffic emissions. However, these benefits can only be achieved with successful matching (sharing) between passengers, which emphasizes the importance of a comprehensive understanding of the matching success rate, i.e., shareability. Despite extensive research into the determinants of shareability, existing literature either relies on simulations and theoretical models with limited empirical validation, or focuses on system-level shareability for the whole market, overlooking the significant spatiotemporal variability of shareability across trips. This study aims to fill these gaps by proposing a path-based model that leverages real-world ridesplitting data to quantify the determinants of shareability at a finer spatiotemporal granularity. Utilizing data from New York City, our results show that: (1) shareability is spatiotemporally heterogeneous; (2) high demand intensity, especially the intensity of medium−/short-distance trips, contributes to greater shareability; (3) the positive contribution of demand intensity diminishes as it increases; (4) a higher road speed improves shareability; (5) excessive one-way street and over-dense street network are related to low shareability. These findings validate and enrich prior findings, which can be used to inform the future development of ridesplitting services.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102120"},"PeriodicalIF":7.1000,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How shareable is your trip? A path-based analysis of ridesplitting trip shareability\",\"authors\":\"Guan Huang , Zhan Zhao , A.G.O. Yeh\",\"doi\":\"10.1016/j.compenvurbsys.2024.102120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>As an emerging sustainable mobility solution, ridesplitting services match passengers in a similar direction with a single vehicle to reduce fleet size, vehicle kilometers traveled and traffic emissions. However, these benefits can only be achieved with successful matching (sharing) between passengers, which emphasizes the importance of a comprehensive understanding of the matching success rate, i.e., shareability. Despite extensive research into the determinants of shareability, existing literature either relies on simulations and theoretical models with limited empirical validation, or focuses on system-level shareability for the whole market, overlooking the significant spatiotemporal variability of shareability across trips. This study aims to fill these gaps by proposing a path-based model that leverages real-world ridesplitting data to quantify the determinants of shareability at a finer spatiotemporal granularity. Utilizing data from New York City, our results show that: (1) shareability is spatiotemporally heterogeneous; (2) high demand intensity, especially the intensity of medium−/short-distance trips, contributes to greater shareability; (3) the positive contribution of demand intensity diminishes as it increases; (4) a higher road speed improves shareability; (5) excessive one-way street and over-dense street network are related to low shareability. These findings validate and enrich prior findings, which can be used to inform the future development of ridesplitting services.</p></div>\",\"PeriodicalId\":48241,\"journal\":{\"name\":\"Computers Environment and Urban Systems\",\"volume\":\"110 \",\"pages\":\"Article 102120\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers Environment and Urban Systems\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0198971524000498\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers Environment and Urban Systems","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0198971524000498","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
How shareable is your trip? A path-based analysis of ridesplitting trip shareability
As an emerging sustainable mobility solution, ridesplitting services match passengers in a similar direction with a single vehicle to reduce fleet size, vehicle kilometers traveled and traffic emissions. However, these benefits can only be achieved with successful matching (sharing) between passengers, which emphasizes the importance of a comprehensive understanding of the matching success rate, i.e., shareability. Despite extensive research into the determinants of shareability, existing literature either relies on simulations and theoretical models with limited empirical validation, or focuses on system-level shareability for the whole market, overlooking the significant spatiotemporal variability of shareability across trips. This study aims to fill these gaps by proposing a path-based model that leverages real-world ridesplitting data to quantify the determinants of shareability at a finer spatiotemporal granularity. Utilizing data from New York City, our results show that: (1) shareability is spatiotemporally heterogeneous; (2) high demand intensity, especially the intensity of medium−/short-distance trips, contributes to greater shareability; (3) the positive contribution of demand intensity diminishes as it increases; (4) a higher road speed improves shareability; (5) excessive one-way street and over-dense street network are related to low shareability. These findings validate and enrich prior findings, which can be used to inform the future development of ridesplitting services.
期刊介绍:
Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.