Network and station-level bike-sharing system prediction: a San Francisco bay area case study-Reference-Cited by-同舟云学术

Network and station-level bike-sharing system prediction: a San Francisco bay area case study

Published:2021-07-08 Issue: Volume: Page:1-11
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Ashqar Huthaifa I.¹^ORCID,Elhenawy Mohammed²^ORCID,Rakha Hesham A.³^ORCID,Almannaa Mohammed⁴^ORCID,House Leanna⁵

Affiliation:

1. Precision Systems, Inc, Washington, DC, USA

2. CARRS-Q, Queensland University of Technology, Kelvin Grove, QLD, Australia

3. Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Tech Transportation Institute, Blacksburg, VA, USA

4. Civil Engineering Department, King Saud University, Riyadh, Saudi Arabia

5. Department of Statistics, Virginia Tech, Blacksburg, VA, USA

Funder

National Science Foundation

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2021.1948412

Reference50 articles.

1. Almannaa, M. H., Ashqar, H. I., Elhenawy, M., Masoud, M., Rakotonirainy, A. & Rakha, H. (2020). A comparative analysis of E-scooter and E-bike usage patterns: Findings from the city of Austin, TX. arXiv preprint arXiv:04033.

2. Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

3. Modeling bike counts in a bike-sharing system considering the effect of weather conditions

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Features that influence bike sharing demand;Heliyon;2024-09

2. Transfer learning for cross-modal demand prediction of bike-share and public transit;Journal of Intelligent Transportation Systems;2024-06-30

3. Prediction of profile error in aspheric grinding of spherical fused silica by ensemble learning regression methods;Precision Engineering;2024-06

4. Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data;International Journal of Computational Intelligence Systems;2024-05-27

5. Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning;Energy;2023-12