Transit Ridership Dynamics and Built Environment Factors
Summary
Transit ridership dynamics encompass the temporal and spatial variations in usage of public transport systems, influenced by a complex interplay of demographic, socio-economic and physical factors. The built environment—comprising land-use patterns, street networks, density, accessibility and amenity provision—shapes travel behaviour by influencing how easily and attractively people can reach transit services. Recent advances have revealed pronounced spatial heterogeneity in ridership responses to changes in land-use composition and transport infrastructure, with nonlinear and threshold effects evident across urban contexts. Modelling approaches, including geographically weighted techniques and machine-learning algorithms, have provided richer insights into localised station-level demand fluctuations, peak-hour deviations and modal shifts under extraordinary events such as pandemics. Understanding these dynamics is critical for integrating land-use planning with transport provision, fostering sustainable mobility, reducing congestion and mitigating environmental impacts. This knowledge underpins policy measures for transit-oriented development, targeted service adjustments and infrastructure investments that respond to evolving travel patterns and support resilient, equitable urban growth.
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Transit Ridership Dynamics and Built Environment Factors publication trend
The graph below shows the total number of articles in transit ridership dynamics and built environment factors across all publications each year (not limited to Nature Index journals).
Technical terms
Built environment: The physical configuration of urban spaces, including land use, street layouts, density and infrastructure that shape mobility and accessibility.
Transit ridership dynamics: The patterns and fluctuations in public transport usage over time and space, reflecting demand responses to multiple factors.
Geographically weighted regression (GWR): A local regression technique that accounts for spatial non-stationarity by estimating location-specific parameter values.
Geographically and temporally weighted regression (GTWR): An extension of GWR that incorporates temporal weighting to model space–time heterogeneity in relationships between variables.
Spatial autocorrelation: The degree to which similar values of a variable cluster or disperse across a geographic area, indicating spatial dependency.
Land use mix: A measure of diversity in land-use types within an area, often associated with multifunctional neighbourhoods and walkable trip generation.
References
- Exploring the association between travel demand changes and the built environment during the COVID-19 pandemic. Smart Construction and Sustainable Cities (2023).
- Understanding the Spatiotemporal Impacts of the Built Environment on Different Types of Metro Ridership: A Case Study in Wuhan, China. Smart Cities (2023).
- Bus ridership and its determinants in Beijing: A spatial econometric perspective. Transportation (2022).
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