City travel patterns in niterói reveal transit and walking trends
Spatial patterns of transportation choice in Niterói
Multiagent Systems
Summary
Understanding how people move around a city usually needs detailed and expensive data. The authors created a computer model that uses general city information to simulate daily travel for about 475,000 made-up residents of Niterói, Brazil. They found that walking is common in the city center, car use grows near the edges, and public transit use is strong along main bus routes. The model shows that making bus stops easier to reach could increase how many people use public transit, especially in far areas.
What this means in practice
- •For urban planners: Use a model to predict how changes in bus stop access affect transit use in different neighborhoods.
- •For transportation modelers: Generate detailed daily trip simulations from aggregated urban data without relying on costly individual travel surveys.
Authors
Sandro Martinelli Reia, Taylor Anderson, Dieter Pfoser
Abstract
Characterizing how people move through a city requires spatially and temporally fine-grained data that are often costly and difficult to obtain. Here, we introduce a computational framework that uses macroscopic, aggregated, and static urban data to construct a microscopic, dynamic, and spatially detailed representation of urban mobility. Building on an existing activity simulator, the framework combines a synthetic population, the built environment, and transport networks to generate daily trips and assign walking, car, or public transit. Applied to approximately 475,000 synthetic residents of Niterói, Brazil, simulated neighborhood-level transit patterns show a strong association with an empirical activity proxy (Pearson r=0.885). Walking is more prevalent in compact central areas, car use increases toward the periphery, and public-transit use remains comparatively high along bus corridors. Reducing walking times to and from bus stops by 30% increases the modeled public-transit-to-car ratio by 11.3%, with larger transit-share gains in several peripheral areas. These results reveal spatial differences in transportation choice and identify where improved transit access may have the greatest modeled effect.