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Inferring trip purposes and demand dynamics in on-demand transit: A heuristic and probabilistic framework

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  • Agrawal, Aman
  • Mishra, Sabyasachee

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.

Suggested Citation

  • Agrawal, Aman & Mishra, Sabyasachee, 2026. "Inferring trip purposes and demand dynamics in on-demand transit: A heuristic and probabilistic framework," Transport Policy, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:trapol:v:181:y:2026:i:c:s0967070x26000752
    DOI: 10.1016/j.tranpol.2026.104065
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