Estimating door-to-door travel time using a synthetic population enriched with smart card data

Abstract

Many modern planning systems aim to reduce average within-city commuting time to 30 min or less, a concept termed the 30-minute city. However, travel time for many large cities exceeds planning targets, and this can subsequently affect commuter health and wellbeing. Although data is often lacking for the ‘first-mile’ and ‘last-mile’ segments of public transport journeys, realistic calculations of the 30-minute city and other chrono-urbanism targets need to include door-to-door travel time. Here, we describe an approach to estimate door-to-door public transport travel time by enriching a synthetic population with smart card data without the need for mobile phone or travel survey data. The algorithm is applied to Sydney, Australia, and is used to measure how effectively a chrono-urbanism target is met in terms of door-to-door travel time compared to travel time without the first- and last-mile. Only 21.7% of validated commuters were modelled to have door-to-door travel time of 30 min or less for the journey to work. This is substantially lower than the 62.0% estimated using calculations without the first- and last-mile.