Impact of Shared E-scooter Introduction on Public Transport Demand: A Case Study in Santiago, Chile
- URL: http://arxiv.org/abs/2409.17814v2
- Date: Fri, 13 Jun 2025 19:10:39 GMT
- Title: Impact of Shared E-scooter Introduction on Public Transport Demand: A Case Study in Santiago, Chile
- Authors: Daniela Opitz, Eduardo Graells-Garrido, Jacqueline Arriagada, Matilde Rivas, Natalia Meza,
- Abstract summary: This study examines how the introduction of shared electric scooters (e-scooters) affects public transport demand in Santiago, Chile.<n>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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning [6.20584161498609]
Bike-sharing systems (BSS) can bridge equity gaps by providing affordable first- and last-mile connections.<n>We introduce Transit for All (TFA), a spatial computing framework designed to guide the equitable expansion of BSS.<n>Using NYC as a case study, we identify transit accessibility gaps that disproportionately impact low-income and minority communities.
arXiv Detail & Related papers (2025-06-18T03:31:07Z) - Multiscale spatiotemporal heterogeneity analysis of bike-sharing system's self-loop phenomenon: Evidence from Shanghai [6.0676675206930035]
This study conducts a multiscale analysis with a spatial autoregressive model and double machine learning framework.<n>Results reveal that bike-sharing self-loop intensity exhibits significant spatial lag effect at street scale.<n>To enhance bike-sharing cooperation, we advocate augmenting bicycle availability in areas with high metro usage and low bus coverage.
arXiv Detail & Related papers (2024-11-26T16:18:38Z) - GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
arXiv Detail & Related papers (2024-08-19T08:23:38Z) - DenseLight: Efficient Control for Large-scale Traffic Signals with Dense
Feedback [109.84667902348498]
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network.
Most prior TSC methods leverage deep reinforcement learning to search for a control policy.
We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
arXiv Detail & Related papers (2023-06-13T05:58:57Z) - iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed
Multi-Agent Reinforcement Learning [57.24340061741223]
We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios.
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
arXiv Detail & Related papers (2023-06-09T20:12:02Z) - Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving.
New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories.
We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
arXiv Detail & Related papers (2023-04-23T16:01:36Z) - Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis
of Micro-mobility Services [19.15684785810306]
We employ a combination of methods that analyse both spatial and temporal characteristics related to e-scooter trips.
Population density is the topmost important feature, and it associates with e-scooter usage positively.
We found that the effect of humidity is more important than precipitation in predicting hourly e-scooter trip count.
arXiv Detail & Related papers (2023-04-18T04:27:56Z) - Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene.
We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
arXiv Detail & Related papers (2021-06-22T15:40:21Z) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention.
Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
arXiv Detail & Related papers (2021-04-19T11:48:13Z) - Do e-scooters fill mobility gaps and promote equity before and during
COVID-19? A spatiotemporal analysis using open big data [7.0445529434309515]
E-scooters have both competing and complementary effects on transit and bikesharing services.
Price premium is greater during the COVID-19 pandemic but the associated travel-time savings are smaller.
E-scooters complement bikesharing and transit by providing services to underserved neighborhoods.
arXiv Detail & Related papers (2021-03-11T03:29:21Z) - Transportation Density Reduction Caused by City Lockdowns Across the
World during the COVID-19 Epidemic: From the View of High-resolution Remote
Sensing Imagery [48.52477000522933]
The COVID-19 epidemic began to worsen in the first months of 2020.
stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread.
We provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities.
arXiv Detail & Related papers (2021-03-02T13:45:16Z) - Unavailable Transit Feed Specification: Making it Available with
Recurrent Neural Networks [8.968417883198374]
In general, the demand for public transport services, with an increasing reluctance to use them, is their quality.
The approach proposed in this paper, using innovative methodologies resorting on data mining and machine learning techniques, aims to make available the unavailable data about public transport.
arXiv Detail & Related papers (2021-02-20T12:17:20Z) - Micromobility in Smart Cities: A Closer Look at Shared Dockless
E-Scooters via Big Social Data [6.001713653976455]
Dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities.
E-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues.
This paper is the first large-scale systematic study on shared e-scooters using big social data.
arXiv Detail & Related papers (2020-10-28T19:59:45Z) - The Benefits of Autonomous Vehicles for Community-Based Trip Sharing [20.51380943801894]
This work reconsiders the concept of community-based trip sharing proposed by Hasan et al.
It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners.
The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%.
arXiv Detail & Related papers (2020-08-28T18:12:13Z) - Polestar: An Intelligent, Efficient and National-Wide Public
Transportation Routing Engine [43.09401975244128]
We present Polestar, a data-driven engine for intelligent and efficient public transportation routing.
Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs.
We then introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectiveness.
arXiv Detail & Related papers (2020-07-11T05:14:52Z) - Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050.
The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
arXiv Detail & Related papers (2020-05-09T13:53:01Z) - Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data [58.720142291102135]
By 2050 two thirds of the world population will reside in urban areas.
This growth is faster and more complex than the ability of cities to measure and plan for their sustainability.
To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions.
arXiv Detail & Related papers (2020-02-20T14:37:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.