Potential for Commuting Demand Management: Analysis of Day-to-Day Modal Variability Based on a 1-Year GPS-Tracking Dataset

Commuting is a major contributor to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>C</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> emissions from passenger transportation. This study examines the potential of commuting demand management (CDM) by analyzing within-person variability in commuting mode choices. We utilized GPS tracking data to monitor the commuting trips of 129 participants in Germany from September 2022 to August 2023. On average, each participant recorded 10 months of data with 106 trips between their “main home” and “main work” location. Our findings indicated that participants typically use three different main modes for their commuting trips. The Herfindahl–Hirschmann index and the difference between the primary and alternative modes revealed differences in the modal stability among commuters, with some showing high variability and others low. Factors such as gender, possession of a driver’s license, and economic status appeared to influence this variability. We further categorized individuals into commuter modal types (active, public, private motorized) per month using k -means clustering. After 5 months, 71.1% of commuters remained in their original modal type cluster (95% CI: 63.6% to 79.4%). However, by the 10-month mark, this figure had decreased to 58.9% (95% CI: 48.8% to 71.0%). This observed variability in commuting mode choices suggests opportunities for CDM. By designing policies that promote the use of sustainable modes already adopted by commuters, CDM could effectively increase the modal share of these environmentally friendly options.