Forecasting usage and bike distribution of dockless bike‐sharing using journey data-Reference-Cited by-同舟云学术

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Forecasting usage and bike distribution of dockless bike‐sharing using journey data

Published:2020-12 Issue:12 Volume:14 Page:1647-1656
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Hua Mingzhuang¹²^ORCID,Chen Jingxu²³,Chen Xuewu¹²,Gan Zuoxian⁴,Wang Pengfei¹²,Zhao De¹²

Affiliation:

1. Jiangsu Key Laboratory of Urban ITSSoutheast UniversityDongnandaxue Road #2NanjingPeople's Republic of China

2. School of Transportation, Southeast UniversityDongnandaxue Road #2NanjingPeople's Republic of China

3. Department of Logistics & Maritime StudiesHong Kong Polytechnic UniversityHung HomKowloonHong Kong

4. College of Transportation EngineeringDalian Maritime UniversityLinghai Road #1DalianPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2020.0305

Reference32 articles.

1. A modeling framework for the dynamic management of free-floating bike-sharing systems

2. Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

3. Short-term demand forecasting for bike sharing system based on machine learning

4. Short-term traffic forecasting: Where we are and where we’re going

5. Predicting station level demand in a bike‐sharing system using recurrent neural networks

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4. Adaptive generative adjustable electric fence method and internal obstacle detection;Transportation Research Part C: Emerging Technologies;2024-05

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