Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia

Abstract

The demand for public transport and commuter behaviour are evidently affected by a series of D-factors relating to the built environment, including density, diversity, design, destination and/or demography. The effect of fare policies, however, in conjunction with built and non-built environment features has not been assessed. Given the 2017 fare policy change introduced in South East Queensland, Australia, this study examines how the policy reform (changes in fares, its structure and incentives) affects public transport ridership. Drawing on two like-for-like periods of transport smart card data before and after the policy reform, we compare the number of card users, journeys, and travel costs under the two fare systems. Through a set of statistical analysis and spatial lag regression, we examine the impact of the fare policy change on ridership, controlled by variations of built and non-built environment features, including population density, land use diversity, demographic features of commuters, distance to the central business district (CBD) and destination accessibility. Our findings show that public transit ridership can be boosted by reducing the fare cost per journey which can then result in overall revenue gain. However, such attraction by fare reduction varies substantially by user groups. Furthermore, the influences of population density, destination accessibility, distance to CBD and demographic features of commuters on ridership are significant (p < 0.01); while the influences of land use diversity and fare change tend to be insignificant compared to the other D-factors. We argue that in order to increase public transport usage policy makers need to consider fare policy reform in conjunction with built environment and demographic factors in order to increase service availability and ensure that services are accessible and affordable to the general public. This study also offers a generic framework that employs big data analytics to assess public policy intervention in the Australian context.