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 c...[ Read more ]

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 understand the inequality in commuting demand distribution with the rank-flow approach and we develop a realistic determinant to measure penalty risks with the time reserved for the last-mile trip. Our findings verify theoretical bottleneck models, aid in the understanding of inequality in distribution, and support policy making, such as flexible working-hour policies to manage peak demand.