Using smart card data to understand travel behaviours, accessibility, Inequity and policy change
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. 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 project also offers a generic framework that employs big data analytics to assess public policy intervention in the Australian context.
This project produced several publications and reports:
2019 UQ Travel Report for internal use.
Liu, Y., Wang, S., & Xie, B. (2019). 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. Transport Policy, 76, 78-89.
Corcoran, J., Pojani, D., Rowe, F., Zhou, J., Kim, J., Wei, M., ... & Liu, Y. (2018). Too wet? Too cold? Too hot? This is how weather affects the trips we make.
Wei, M., Liu, Y., & Sigler, T. J. (2015, December). An exploratory analysis of Brisbane’s commuter travel patterns using smart card data. In Proceedings of the State of Australian Cities Research Network; State of Australian Cities National Conference, Gold Coast, Australia (pp. 9-11).
Maddox, C., Corcoran, J., & Liu, Y. (2013). Mapping spatial flows over time: a case study using journey-to-work data. Journal of Spatial Science, 58(1), 147-159.
Wei, M., Liu, Y., Sigler, T., Liu, X., & Corcoran, J. (2019). The influence of weather conditions on adult transit ridership in the sub-tropics. Transportation Research Part A: Policy and Practice, 125, 106-118.
Wei, M., Corcoran, J., Sigler, T., & Liu, Y. (2018). Modeling the influence of weather on transit ridership: A case study from Brisbane, Australia. Transportation Research Record, 2672(8), 505-510.
Zhang, M., & Liu, Y. (2015, January). An exploratory analysis of Bus Rapid Transit on property values: A case study of Brisbane’s South East Busway. In State of Australian Cities National Conference, 2015, Gold Coast, Queensland, Australia.