Optimizing Shared E-Scooter Operations Under Demand Uncertainty: A Framework Integrating Machine Learning and Optimization Techniques-Reference-Cited by-同舟云学术

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Optimizing Shared E-Scooter Operations Under Demand Uncertainty: A Framework Integrating Machine Learning and Optimization Techniques

Published:2024 Issue: Volume:12 Page:26957-26977
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Saum Narith¹^ORCID,Sugiura Satoshi¹^ORCID,Piantanakulchai Mongkut²^ORCID

Affiliation:

1. Division of Engineering and Policy for Sustainable Environment, Hokkaido University, Sapporo, Hokkaido, Japan

2. School of Civil Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10436102.pdf?arnumber=10436102

Reference87 articles.

1. A review on an emerging new mode of transport: The shared dockless electric scooter;Saum

2. Exploring best practice for municipal e-scooter policy in the United States

3. Real-time inventory control and rebalancing in bike-sharing systems by using a stochastic Petri net model

4. Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare

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