A data driven approach to update public transport service elasticities

Abstract

Understanding the passenger demand impacts of public transport service changes is a fundamental aspect of transport planning. The main objective of this study is to derive an updated Generalised Journey Time (GJT) elasticity for urban and metropolitan public transport networks, by applying a revealed preference approach using individual passenger journey data. Based on more than 25 million empirical journeys subject to 9 different service interventions within the Greater London area, we find an average GJT elasticity of −0.61. The value implies that for every 1% increase in generalised journey time, on average public transport demand is expected to reduce by 0.61%, and vice versa. We also find that the demand response to service changes is most elastic during the midday period between the peak hours and most inelastic during the AM peak and early morning, possibly caused by a higher share of mandatory journeys. Our study results confirm the existence of a build-up rate from the initial short-run elasticity to a somewhat stronger longer-run elasticity. Besides, we find that at least within the short- and medium-term demand is more elastic to service degradations compared to service improvements. Our findings imply that it requires more time for demand to increase in response to a service quality improvement, compared to demand to decrease after a service quality reduction.