Engaging the Crowd in Sensing for Smart Mobility: A Discrete Choice Experiment

van Den Boogert, Ria Johanna; Ding, Aaron Yi

doi:10.1109/ojits.2023.3277311

IEEE Open J. Intell. Transp. Syst.

2023

DOI: 10.1109/ojits.2023.3277311

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Engaging the Crowd in Sensing for Smart Mobility: A Discrete Choice Experiment

Ria Johanna van Den Boogert,

Aaron Yi Ding

Abstract: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Human Research Ethics Committee TU Delft under Application No. 1824, and performed in line with the GDPR.

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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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A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Engaging the Crowd in Sensing for Smart Mobility: A Discrete Choice Experiment

Abstract: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Human Research Ethics Committee TU Delft under Application No. 1824, and performed in line with the GDPR.

Cited by 1 publication

References 50 publications

A Systematic Literature Review on Machine Learning in Shared Mobility

A Systematic Literature Review on Machine Learning in Shared Mobility

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