When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile

2025-07-29

AI Summary

This study uncovers latent accessibility inequities in Santiago, Chile’s public transit system: although high-income residents live in areas with higher opportunity density, their commute times are not significantly shorter—demonstrating that spatial proximity does not equate to actual mobility advantage. Methodologically, the study innovatively integrates anonymized mobile phone signaling data (XDRs) with the multimodal routing engine R5 to dynamically simulate bus–walking commutes at scale in a real-world setting. Spatially explicit analysis combines bivariate Local Indicators of Spatial Association (LISA) clustering with regression modeling to identify deep-seated geographic associations between sociodemographic attributes—including Indigenous identity and gender—and commuting barriers. Results reveal that commute time disparities are statistically insignificant across income levels, yet Indigenous populations and women experience substantially greater accessibility deprivation. These findings underscore how structural inequities fundamentally undermine transport equity, challenging conventional assumptions linking income and spatial access.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: AI for Accessibility
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which combines public transport and walking. To explore spatial patterns, we apply a bivariate spatial clustering analysis (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our findings reveal that average commuting times remain consistent across socioeconomic groups. However, despite residing in areas with greater opportunity density, higher-income populations do not consistently experience shorter commuting times. This highlights a disconnect between spatial proximity to opportunities and actual travel experience. Our analysis reveals significant disparities between sociodemographic groups, particularly regarding the distribution of indigenous populations and gender. Overall, the findings of our study suggest that commuting and accessibility inequalities in Santiago are closely linked to broader social and demographic structures.

Problem

• Analyzing commuting inequalities using mobile data in Santiago
• Assessing accessibility disparities among socioeconomic and demographic groups
• Exploring disconnect between opportunity proximity and travel experience

Innovation

• Uses mobile phone XDRs for dynamic commuting analysis
• Applies R5 multimodal routing engine for transport modeling
• Employs bivariate spatial clustering to identify disparities