Uncovering physical activity trade-offs in transportation policy: A spatial agent-based model of Bogotá, Colombia

Abstract

Background: Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity.

Methods: The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios.

Results: Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups.

Conclusions: Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

Keywords: Agent-based model; Complex systems; Health inequities; Physical activity; Time scarcity; Transportation policy.

Fig. 1

Map of the City of Bogotá showing the extent and distribution of the TransMilenio bus rapid transit system and the six SES strata in the city (left) along with the abstract representation of the city in the ABM environment (right)

Fig. 2

Baseline calibrated model fit to percent mode share data from the 2019 Bogotá Household Travel Survey. Each facet represents a population subgroup, ranging from overall, city-level mode share (facet 1) to SES-level mode share (columns 2–7). Within each facet, the left-hand bar depicts mode share patterns observed in the 2019 Bogotá Household Travel Survey, while the right-hand bar shows the model simulated mode share. The different colors represent different modes of transport. Notably, the Travel Survey also captures ‘Other’ modes of travel used in Bogotá (e.g., cable car, taxi etc.) that were not simulated by the model

Fig. 3

Absolute percent change in mode share (y-axis), by mode, overall and by SES (x-axis) following the implementation of A a 30% fare subsidy only; B free bus and BRT travel for all, a congestion tax of 2,000 COP only (C) and 20,000 COP only (F), and a combination of these fare subsidies and congestion taxes (D, E, G, H)

Fig. 4

Absolute change in median active and total travel time, in minutes (y-axis), by mode, overall and by SES (x-axis) following the implementation of A a 30% fare subsidy only; B free bus and BRT travel for all, a congestion tax of 2,000 COP only (C) and 20,000 COP only (F), and a combination of these fare subsidies and congestion taxes (D, E, G, H)

Fig. 5

Distribution of times spent walking to work each day (red line signifies median), overall and by SES for each scenario; the baseline model (blue section A no policy intervention), the congestion taxes, including 2,000 COP (B) and 20,000 COP tax (C), and the fare subsidies; 30% fare subsidy (D); and free public transport for all users (E)

Fig. 6

Absolute change in percent commuters meeting weekly recommended physical activity guidelines, overall and by SES (x-axis) following the implementation of A a 30% fare subsidy only; B free bus and BRT travel for all, a congestion tax of 2,000 COP only (C) and 20,000 COP only (F), and a combination of these fare subsidies and congestion taxes (D, E, G, H)