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Transit planning is a complex yet crucial component for any public transit system. The key prerequisite to transit planning is the estimation of demand. Travel demand is one of the key inputs in solving problems like routing and scheduling. Considering the dynamic, spatially and temporally, nature of travel demand, it becomes essential for the transit organisations to adapt real-time travel demand estimation with the help of Automated Data Collection Systems (ADCS). The current work explores the use of Electronic Ticketing Machines (ETMs) as a potential source of passenger demand data. Demand in the form of Origin–Destination (OD) matrices is generated from the ETM data. The ultimate objective of this study is to assess the full potential of ETM data in transit planning. Python programming is selected for its comprehensibility and strong data analytic abilities.

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ETM Data-Based OD Matrices for Public Transit Planning

  • 会议论文

  • 发表日期：

    2025年

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  • Sheethal Pavan Puthra Muvvala,
  • M. V. L. R. Anjaneyulu

摘要

Transit planning is a complex yet crucial component for any public transit system. The key prerequisite to transit planning is the estimation of demand. Travel demand is one of the key inputs in solving problems like routing and scheduling. Considering the dynamic, spatially and temporally, nature of travel demand, it becomes essential for the transit organisations to adapt real-time travel demand estimation with the help of Automated Data Collection Systems (ADCS). The current work explores the use of Electronic Ticketing Machines (ETMs) as a potential source of passenger demand data. Demand in the form of Origin–Destination (OD) matrices is generated from the ETM data. The ultimate objective of this study is to assess the full potential of ETM data in transit planning. Python programming is selected for its comprehensibility and strong data analytic abilities.