An evaluation of transport mode shift policies on transport-related physical activity through simulations based on random forests

Int J Behav Nutr Phys Act. 2017 Oct 23;14(1):143. doi: 10.1186/s12966-017-0600-1.


Background: Physical inactivity is widely recognized as one of the leading causes of mortality, and transport accounts for a large part of people's daily physical activity. This study develops a simulation approach to evaluate the impact of the Ile-de-France Urban Mobility Plan (2010-2020) on physical activity, under the hypothesis that the intended transport mode shifts are realized.

Methods: Based on the Global Transport Survey (2010, n = 21,332) and on the RECORD GPS Study (2012-2013, n = 229) from the French capital region of Paris (Ile-de-France), a simulation method was designed and tested. The simulation method used accelerometer data and random forest models to predict the impact of the transport mode shifts anticipated in the Mobility Plan on transport-related moderate-to-vigorous physical activity (T-MVPA). The transport mode shifts include less private motorized trips in favor of more public transport, walking, and biking trips.

Results: The simulation model indicated a mean predicted increase of 2 min per day of T-MVPA, in case the intended transport mode shifts in the Ile-de-France Urban Mobility Plan were realized. The positive effect of the transport mode shifts on T-MVPA would, however, be larger for people with a higher level of education. This heterogeneity in the positive effect would further increase the existing inequality in transport-related physical activity by educational level.

Conclusions: The method presented in this paper showed a significant increase in transport-related physical activity in case the intended mode shifts in the Ile-de-France Urban Mobility Plan were realized. This simulation method could be applied on other important health outcomes, such as exposure to noise or air pollution, making it a useful tool to anticipate the health impact of transport interventions or policies.

Keywords: Active transport; Data integration; France; Health inequalities; Machine learning; RECORD cohort study; Simulation study; Transport.

MeSH terms

  • Air Pollution / statistics & numerical data
  • Bicycling / statistics & numerical data
  • Educational Status
  • Exercise*
  • France
  • Health Promotion
  • Humans
  • Paris
  • Policy
  • Surveys and Questionnaires
  • Transportation / methods*
  • Walking / statistics & numerical data