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.

本文使用英国剑桥的自动车牌识别（ANPR）行程链数据研究了通勤者的非上下班旅行需求。开发了一种新颖的基于规则的算法，用于识别通勤车辆和相关的非通勤行程。鉴定结果通过外部数据进行验证。根据旅行概率，平均旅行频率，持续时间和需求弹性来研究非上下班旅行需求。该研究发现，首先，非通勤旅行是汽车通勤者出行需求的重要来源–在日常旅行链中从事非通勤活动的汽车通勤者平均要花费大约2.7小时，包括出行时间。在剑桥的典型工作日。第二，工作时间越长，参与非通勤旅行的可能性就越低，这意味着在每日旅行时间预算内会有替代作用。最后，就旅行需求弹性而言，早于早上（9–9am）和午休期间（12-3pm）的非通勤旅行的弹性较差。需求弹性的变化很可能归因于与某些旅行目的相关的不同旅行约束。得出了大流行后交通需求和管理的含义。需求弹性的变化很可能归因于与某些旅行目的相关的不同旅行约束。得出了大流行后交通需求和管理的含义。需求弹性的变化很可能归因于与某些旅行目的相关的不同旅行约束。得出了大流行后交通需求和管理的含义。