A causal evaluation of Bogota's cable car illustrates the transformative potential of mobile phone data for policy analysis
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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
What is the people posting about symptoms related to Coronavirus in Bogota, Colombia?
During the last months, there is an increasing alarm about a new mutation of coronavirus, covid-19 coined by World Health Organization(WHO) with an impact in many areas: economy, health, politics and others. This situati…
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics,…
Causal InferenceDrug DiscoveryLanguage ModelingLanguage Modelling+1Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation. Adversarial perturbation, used to identify vulnerabilities i…
Adversarial RobustnesscounterfactualFairnessMetric LearningA Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009
With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a fore…
Causal InferenceTime SeriesTime Series ForecastingUnconditional Randomization Tests for Interference
When conducting causal inference or designing policy, researchers are often concerned with the existence and extent of interference between units, which may be influenced by factors such as distance, proximity, and conne…
Causal Inferencevalid