Jacqueline Arriagada | Searcharxiv

arXiv2025

Impact of Shared E-scooter Introduction on Public Transport Demand: A Case Study in Santiago, Chile

This study examines how the introduction of shared electric scooters (e-scooters) affects public transport demand in Santiago, Chile, analyzing whether they complement or substitute for existing transit services. We used smart card data from the integrated public transport system of Santiago and GPS traces from e-scooter trips during the initial deployment period. We employed a difference-in-differences approach with negative binomial regression models across three urban regions identified through k-means clustering: Central, Intermediate, and Peripheral. Results reveal spatially heterogeneous effects on public transport boardings and alightings. In the Central Region, e-scooter introduction was associated with significant substitution effects, showing a 23.87% reduction in combined bus and metro boardings, suggesting e-scooters replace short public transport trips in high-density areas. The Intermediate Region showed strong complementary effects, with a 33.6% increase in public transport boardings and 4.08% increase in alightings, indicating e-scooters successfully serve as first/last-mile connectors that enhance transit accessibility. The Peripheral Region exhibited no significant effects. Metro services experienced stronger impacts than bus services, with metro boardings increasing 9.77\% in the Intermediate Region. Our findings advance understanding of micromobility-transit interactions by demonstrating that both substitution and complementarity can coexist within the same urban system, depending on local accessibility conditions. These results highlight the need for spatially differentiated mobility policies that recognize e-scooters' variable roles across urban environments.

cs.CY↗

arXiv2023

Feel Old Yet? Updating Mode of Transportation Distributions from Travel Surveys using Data Fusion with Mobile Phone Data

Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorization techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit, motorised, active, and taxi. Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. We validate our findings comparing our updated estimates with official smart card transaction data.

cs.CY↗