{"title":"利用可解释机器学习探索电动滑板车与骑行服务关系的空间异质性","authors":"Junfeng Jiao, Yiming Xu, Yang Li","doi":"10.1016/j.trd.2024.104452","DOIUrl":null,"url":null,"abstract":"<div><div>The expansion of e-scooter sharing system has introduced several novel interactions within the existing transportation system. However, few studies have explored how spatial contexts influence these interactions. To fill this gap, this study explored the spatial heterogeneity in e-scooter’s relationship with ridesourcing using data from Chicago, IL. We developed a Light Gradient Boosting Machine (LightGBM) to estimate e-scooter sharing usage using ridesourcing trips along with associated built environment and socio-demographic variables. The model was interpreted using SHapley Additive exPlanations (SHAP). Results indicated that the threshold effects, where the positive relationship between e-scooter sharing and ridesourcing significantly weakened beyond a certain value, were more pronounced in areas with lower population density, fewer jobs, and fewer young, highly educated population. This is primarily attributed to the limited competitiveness of e-scooter sharing in these areas. These findings can assist cities in harmonizing e-scooter sharing and ridesourcing thus promoting sustainable transportation systems.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring spatial heterogeneity of e-scooter’s relationship with ridesourcing using explainable machine learning\",\"authors\":\"Junfeng Jiao, Yiming Xu, Yang Li\",\"doi\":\"10.1016/j.trd.2024.104452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The expansion of e-scooter sharing system has introduced several novel interactions within the existing transportation system. However, few studies have explored how spatial contexts influence these interactions. To fill this gap, this study explored the spatial heterogeneity in e-scooter’s relationship with ridesourcing using data from Chicago, IL. We developed a Light Gradient Boosting Machine (LightGBM) to estimate e-scooter sharing usage using ridesourcing trips along with associated built environment and socio-demographic variables. The model was interpreted using SHapley Additive exPlanations (SHAP). Results indicated that the threshold effects, where the positive relationship between e-scooter sharing and ridesourcing significantly weakened beyond a certain value, were more pronounced in areas with lower population density, fewer jobs, and fewer young, highly educated population. This is primarily attributed to the limited competitiveness of e-scooter sharing in these areas. These findings can assist cities in harmonizing e-scooter sharing and ridesourcing thus promoting sustainable transportation systems.</div></div>\",\"PeriodicalId\":23277,\"journal\":{\"name\":\"Transportation Research Part D-transport and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part D-transport and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1361920924004097\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part D-transport and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361920924004097","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Exploring spatial heterogeneity of e-scooter’s relationship with ridesourcing using explainable machine learning
The expansion of e-scooter sharing system has introduced several novel interactions within the existing transportation system. However, few studies have explored how spatial contexts influence these interactions. To fill this gap, this study explored the spatial heterogeneity in e-scooter’s relationship with ridesourcing using data from Chicago, IL. We developed a Light Gradient Boosting Machine (LightGBM) to estimate e-scooter sharing usage using ridesourcing trips along with associated built environment and socio-demographic variables. The model was interpreted using SHapley Additive exPlanations (SHAP). Results indicated that the threshold effects, where the positive relationship between e-scooter sharing and ridesourcing significantly weakened beyond a certain value, were more pronounced in areas with lower population density, fewer jobs, and fewer young, highly educated population. This is primarily attributed to the limited competitiveness of e-scooter sharing in these areas. These findings can assist cities in harmonizing e-scooter sharing and ridesourcing thus promoting sustainable transportation systems.
期刊介绍:
Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution.
We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.