New metro system and active travel: A natural experiment

Background: We used the first metro system in a developing city as a natural experiment to investigate the causal inference in the new metro's impact on modal shift and active travel.

Material and methods: The treatment group was formed by residents from neighbourhoods located within the 800-m walking distance to new metro stations. The first control group was formed by residents lived 1.6 km away from and outside of walking distance to the nearest station, and the second was 5 km away and outside of cycling distance. The groups were determined by local transit-oriented planning practice and empirical studies on active travel. Of the 5627 participants who had finished a baseline travel behaviour survey before new metro launched, 1770 returned and completed the follow-up survey a year after the metro's operation, which consists of 833 cohort participants in the treatment group and 937 in the two types of control groups. We used a difference-in-difference method to make before and after comparisons of travel behaviour changes between treatment and control groups.

Results: Our longitudinal data analyses revealed diverse travel behaviour changes. In general, people who used to take bus have adopted metro. The average metro usage was 30.9 (28.8-33.3) minutes daily for work trips and 16.6 (14.9-18.7) minutes daily for non-work trips. Walking time decreased 19.7 minutes at most (p < 0.001), and cycling decreased 22.1 minutes daily (p < 0.001). Car and e-bike usages remained largely unchanged before and after new metro, without difference between treatment and control groups.

Conclusion: The natural experiment study provided the first empirical evidence in a developing city context on causal inference in new metro's impact on active travel. A new metro does not necessarily promote active travel increase or car use reduction, calling for caution in making general assumptions about the effects of urban rail transit investments. We suggest local urban and transport planning knowledge could be useful in designing and explaining the complex natural experiments in transport and health.