{"title":"Inferring trip purposes and demand dynamics in on-demand transit: A heuristic and probabilistic framework","authors":"Aman Agrawal, Sabyasachee Mishra","doi":"10.1016/j.tranpol.2026.104065","DOIUrl":null,"url":null,"abstract":"<div><div>On-demand transit (ODT) offers a flexible public transportation option that ensures mobility in underserved areas. However, the service is prone to failure due to inadequate planning of demand dynamics and market research. This study examines the influence of trip and neighborhood characteristics on ODT demand across various trip purposes, using data from a service provider in Downtown Memphis, Tennessee, USA. A novel heuristic and probabilistic trip inference method classifies ODT trips by purpose, incorporating variable walking radii to identify destination points of interest for trips with walking as the first and last mile. A two-stage nested logit model analyzes home-based (e.g., work, education, shopping) and non-home-based trips, revealing that wait times, trip distance, passenger numbers, and temporal factors like peak hours vary systematically across trip purposes. Neighborhoods with diverse racial groups (other than Black or White) show high ODT adoption, but areas with a high proportion of residents below the poverty line exhibit low usage, raising equity concerns. This research provides valuable insight for transportation planners and policymakers to make informed decisions in designing sustainable and inclusive ODT services.</div></div>","PeriodicalId":48378,"journal":{"name":"Transport Policy","volume":"181 ","pages":"Article 104065"},"PeriodicalIF":6.3000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transport Policy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967070X26000752","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
On-demand transit (ODT) offers a flexible public transportation option that ensures mobility in underserved areas. However, the service is prone to failure due to inadequate planning of demand dynamics and market research. This study examines the influence of trip and neighborhood characteristics on ODT demand across various trip purposes, using data from a service provider in Downtown Memphis, Tennessee, USA. A novel heuristic and probabilistic trip inference method classifies ODT trips by purpose, incorporating variable walking radii to identify destination points of interest for trips with walking as the first and last mile. A two-stage nested logit model analyzes home-based (e.g., work, education, shopping) and non-home-based trips, revealing that wait times, trip distance, passenger numbers, and temporal factors like peak hours vary systematically across trip purposes. Neighborhoods with diverse racial groups (other than Black or White) show high ODT adoption, but areas with a high proportion of residents below the poverty line exhibit low usage, raising equity concerns. This research provides valuable insight for transportation planners and policymakers to make informed decisions in designing sustainable and inclusive ODT services.
期刊介绍:
Transport Policy is an international journal aimed at bridging the gap between theory and practice in transport. Its subject areas reflect the concerns of policymakers in government, industry, voluntary organisations and the public at large, providing independent, original and rigorous analysis to understand how policy decisions have been taken, monitor their effects, and suggest how they may be improved. The journal treats the transport sector comprehensively, and in the context of other sectors including energy, housing, industry and planning. All modes are covered: land, sea and air; road and rail; public and private; motorised and non-motorised; passenger and freight.