The disparity between rapid urbanization and limited service supplies has raised significant societal concerns, such as overcrowding, caused by a surfeit of individuals traveling at the same time. However, our understanding of how people decide the timing of their trips remains incomplete. Here we use anonymized smart card transaction data from mass transit railway (MTR) systems across three cities to study how commuters schedule travel time to arrive at their workplaces on time. We find two metrics—defined to scale commuters’ time scheduling preferences by investigating relationships among MTR station entry, exit time and work start time—can well indicate arrival penalty risks (early arrival, late arrival, and no penalty), and is common among varying work start times across different cities. Additionally, we explore the varying attractiveness of origin–destination (OD) station pairs to commuters with a rank-flow approach and we develop a realistic determinant to measure the penalty risks with the time reserved for the last-mile trip. Our findings verify theoretical bottleneck models, aid in the understanding of distribution of commuting demand and land uses, and support policy making, such as flexible working-hour policies for peak demand managements.

快速的城市化和有限的服务供应之间的差距引起了重大的社会问题，例如由于同时出行的人数过多而造成的过度拥挤。然而，我们对人们如何决定旅行时间的理解仍然不完整。在这里，我们使用来自三个城市的地铁 (MTR) 系统的匿名智能卡交易数据来研究通勤者如何安排出行时间以准时到达工作场所。我们发现两个指标——通过调查地铁进站、出站时间和上班时间之间的关系来衡量通勤者的时间安排偏好——可以很好地表明到达惩罚风险（早到、迟到和无惩罚），并且在不同城市的上班时间各不相同。此外，我们通过排名流方法探讨了出发地-目的地（OD）车站对对通勤者的不同吸引力，并开发了一个现实的决定因素来衡量最后一英里行程预留时间的处罚风险。我们的研究结果验证了理论瓶颈模型，有助于理解通勤需求和土地使用的分布，并支持政策制定，例如高峰需求管理的灵活工作时间政策。