Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

Hsieh, Hsun-Ping; Lin, Fandel; Jiang, Jiawei; Kuo, Tzu-Ying; Chang, Yu-En

doi:10.3390/app11156748

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…Demand prediction models are different from traditional time series analysis, as they consider both spatial and external factors. Association Rule Learning [182] Clustering [183,184]…”

Section: ) Demand Predictionmentioning

confidence: 99%

“…Linear Model [185,186,187] Tree-based Ensembles [184,186,188,189] Neural Networks [190,191,192] Scooter Dockless OD + Neural Networks [193] For example, demand in one area can be affected by traffic in other areas, and external factors such as weather, events, and holidays can have an impact on demand throughout all regions. Despite significant research in traffic forecasting, spatio-temporal forecasting remains an area of ongoing study.…”

Section: Generalizedmentioning

confidence: 99%

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A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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“…Demand prediction models are different from traditional time series analysis, as they consider both spatial and external factors. Association Rule Learning [182] Clustering [183,184]…”

Section: ) Demand Predictionmentioning

confidence: 99%

Section: Generalizedmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…However, since BSSs are very recent in many regions, there is a lack of research analyzing the demand patterns of modal shift or trip generation in an already operating system. Few studies examine the expansion of existing BSS (Hsieh et al, 2021;Zhang et al, 2016), and others consider latent (or potential) demand only when planning for a new BSS (Frade & Ribeiro, 2014;Krykewycz et al, 2010). Therefore, little has been done on the usage and behavior of current users of an already operating system (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Moura et al_TransportRxiv_GIRA demand_2022.pdf

Moura¹,

Valença²,

Félix³

et al. 2022

Preprint

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<p>Bike-sharing systems allow occasional and regular users to move by replacing other transport modes for the same trip or generating a new journey. Our research assesses the demand for Lisbon's public dock-based bike-sharing system (BSS) users named after GIRA. This paper aims to identify the determinant factors that influence the potential of the BSS to generate new trips or replace previous modes using a binary logit model based on a survey of 3112 BSS users. The survey results indicate that GIRA generated approximately 20% of the BSS trips, i.e., they would not have been realized if GIRA did not exist. The remaining BSS trips replaced other motorized (55%) and non-motorized (25%) trips. The main determinants explaining a higher likelihood of replacing other modes are having a yearly GIRA pass and a bike-sharing station within a 5-min walking distance. In contrast, regular car users are more likely to generate new trips, suggesting they use bike-sharing for recreational purposes. The findings provide policymakers with an assessment of the determinants that can influence bike-sharing users to generate or substitute trips from other modes for bike-sharing and, consequently, give policies to potentially increase bike-sharing mobility share.</p>

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“…The prediction methods are also adopted in vehicle sharing systems. In this framework, the work in [6] deals with the long-term prediction of bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas.…”

mentioning

confidence: 99%