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

TL;DR

This paper introduces a new measurement framework to analyze the regularity of public transit departure times, revealing insights into individual habits and system-wide dynamics over a year-long dataset.

Contribution

It develops a novel 'peakedness' metric for departure times, enabling detailed analysis of individual and collective travel habits in public transit systems.

Findings

Departure time peakedness is more influenced by passenger type than external factors.

Individual habits show long-term evolution, while system-wide peakedness remains stable.

The framework effectively decomposes system dynamics into individual behaviors and their alignment.

Abstract

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 is of critical importance to enable the 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.