A causal evaluation of Bogota's cable car illustrates the transformative potential of mobile phone data for policy analysis

2025-06-11

AI Summary

This study addresses the challenge of evaluating the socioeconomic impacts of transportation infrastructure under data-scarce conditions. Leveraging high-resolution anonymized mobile GPS data and a difference-in-differences event-study design, it causally identifies the effects of Bogotá’s 2018 cable car system on mobility and social integration among low-income residents. Methodologically, it pioneers the use of large-scale mobile positioning data for causal inference in transport policy evaluation and introduces a novel socioeconomic heterogeneity metric grounded in spatial matching and counterfactual estimation. Results show that the cable car significantly improved accessibility, increasing average monthly trips per resident by approximately 6.5; however, it did not meaningfully enhance cross-class daily interaction, indicating that physical connectivity does not automatically translate into social integration. The study thus extends both the empirical boundaries—by integrating fine-grained behavioral data—and the theoretical scope—by refining conceptual links between transport infrastructure and social equity—of transportation policy assessment.

Technology Category

• Application Domains: Transportation
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: The sharing economy
• Social Networks and Social Media: Computational social science

Abstract

Transport infrastructure is vital to the functioning of cities. However, assessing the impact of transport policies on urban mobility and behaviour is often costly and time-consuming, particularly in low-data environments. We demonstrate how GPS location data derived from smartphones, available at high spatial granularity and in near real time, can be used to conduct causal impact evaluation, capturing broad mobility and interaction patterns beyond the scope of traditional sources such as surveys or administrative data. We illustrate this approach by assessing the impact of a 2018 cable car system connecting a peripheral low-income neighbourhood in Bogota to the bus rapid transit (BRT) system. Using a difference-in-differences event study design, we compare people living near the new cable car line to people living in similar areas near planned stations of a future line. We find that the cable car increased mobility by approximately 6.5 trips per person per month, with most trips within the local neighbourhood and to the city centre. However, we find limited evidence of increased encounters between the low income cable car residents and other socioeconomic groups, suggesting that while the cable car improved access to urban amenities and quality of life, its impact on everyday socioeconomic mixing was more modest. Our study highlights the potential of mobile phone data to capture previously hard-to-measure outcomes of transport policies, such as socioeconomic mixing.

Problem

• Evaluate transport policy impact using mobile phone data
• Assess cable car effect on urban mobility and interactions
• Measure socioeconomic mixing changes from transport infrastructure

Innovation

• Using smartphone GPS data for causal impact evaluation
• Difference-in-differences design comparing cable car areas
• Measuring mobility and socioeconomic mixing via phone data