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

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.

Highlights • A novel rule-based algorithm for identifying commuting vehicles and associated non-commuting trips. • Identification results validated with external data and sources of errors investigated. • Substitution effect between commuting and non-commuting trips confirmed. • Different non-commuting travel demand elasticities across the time of the day. • Implications for post-pandemic traffic demand and management.