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Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge

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  • Wan, Li
  • Tang, Junqing
  • Wang, Lihua
  • Schooling, Jennifer

Abstract

The paper investigates the non-commuting travel demand of car commuters using Automatic Number Plate Recognition (ANPR) trip chain data in Cambridge, UK. A novel rule-based algorithm is developed for identifying commuting vehicles and the associated non-commuting trips. Identification results are validated with external data. Non-commuting travel demand is investigated in terms of trip probability, average trip frequency, duration and demand elasticity. The study finds that, first, non-commuting trips represent a significant source of travel demand for car commuters – car commuters who engage in non-commuting activities in their daily trip chains would on average spend approximately 2.7hr on those activities including travel time on a typical workday in Cambridge. Second, longer working hours are associated with a lower probability of engaging in non-commuting trips, implying a substitution effect within the daily travel time budget. Last, in terms of travel demand elasticity, non-commuting trips starting in the early morning (6–9am) are less elastic than those starting in the morning (9–12am) and during the lunch break (12-3pm). The varying demand elasticities are likely to be attributed to the different travel constraints associated with certain trip purposes. Implications for post-pandemic traffic demand and management are drawn.

Suggested Citation

  • Wan, Li & Tang, Junqing & Wang, Lihua & Schooling, Jennifer, 2021. "Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge," Transport Policy, Elsevier, vol. 106(C), pages 76-87.
    • Handle: RePEc:eee:trapol:v:106:y:2021:i:c:p:76-87
    DOI: 10.1016/j.tranpol.2021.03.021
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