Decomposing journey times on urban metro systems via semiparametric mixed methods
mode - subway/metro, place - urban, place - europe, technology - passenger information, technology - automatic vehicle monitoring, operations - reliability, operations - performance, ridership - demand
Public transport, AFC data, AVL data, Revealed preference, Semiparametric regression
The availability of automated data for urban metro systems allows operators to accurately measure journey time reliability. However, there remains limited understanding of the causes of journey time variance and how journey time performance can be improved. In this paper, we present a semiparametric regression modelling framework to determine the underlying drivers of journey time variance in urban metro systems, using the London Underground as a case study. We merge train location and passenger trip data to decompose total journey times into three constituent parts: access times as passengers enter the system, on-train times, and egress times as passengers exit at their destinations. For each journey time component, we estimate non-linear functional relationships which we then use to derive elasticity estimates of journey times with respect to service supply and demand factors, including operational and physical characteristics of metros as well as passenger demand and passenger-specific travel characteristics. We find that the static fixed physical characteristics of stations and routes have the greatest influence on journey time, followed by train speeds, and headways, for which the average elasticities of total journey time are −0.54 and 0.05, respectively. The results of our analysis could inform operators about where potential interventions should be targeted in order to improve journey time performance.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Singh, R., Hörcher, D., Graham, D.J., & Anderson, R.J. (2020). Decomposing journey times on urban metro systems via semiparametric mixed methods. Transportation Research Part C: Emerging Technologies, Vol. 114, pp. 140-163.