Understanding everyday public transit travel habits: A measurement framework for the peakedness of departure time distributions

Persuasive scholarship presents how individual daily travel habits implicate congestion, environmental pollution, and the travel experience. However, the empirical characteristics and dynamics of travel habits remain poorly understood. Quantifying both our individual travel habits and how these habits aggregate to form system-wide dynamics are of critical importance to enable smart design of public transit systems that are better tailored to our daily mobility needs. We contribute to this need through the development and implementation of a new measurement framework capturing the ‘peakedness’ of users’ departure time distributions. Departure time ‘peakedness’ reflects a user’s tendency to repeatedly choose the same departure time for a given origin–destination trip, offering a clearer and more intuitive representation of regularity and habitual patterns compared to traditional metrics like standard deviation or entropy. Our framework demonstrates that system-wide departure time peakedness can be decomposed into individual users’ departure time peakedness and the alignment of their peak times. This allows for a systematic analysis of both individual and collective behaviours. We apply our framework to departure time data from a 12-month period, encompassing 5,947,907 bus journeys made by 29,640 individuals across three urban networks within a large regional metropolis. Our findings reveal that departure time peakedness is more deeply tied to inherent, passenger-specific characteristics, such as passenger type, rather than external factors like weather or holidays. Additionally, individual-level departure time peakedness shows notable dynamics over time, indicating that habitual routines can evolve in the long term, while system-level peakedness exhibits remarkable long-term stability. • Measuring the ‘peakedness’ in departure time distributions to analyse travel habits. • Capturing a user’s tendency towards choosing the same departure time repeatedly. • Proposing indicators that are simple and easy to apply, yet insightful. • Revealing how user-level departure time peakedness shapes system-wide demand loads. • Empirical insights derived from an extensive public transport smartcard dataset.