Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors-Reference-Cited by-同舟云学术

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Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors

Published:2023-02-20 Issue: Volume: Page:1-16
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Li Siteng¹²³,Yang Hang¹²³,Cheng Rongjun¹²³,Ge Hongxia¹²³

Affiliation:

1. Faculty of Maritime and Transportation, Ningbo University, Ningbo, China

2. Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing, China

3. The Sub-centre of National Traffic Management Engineering and Technology Research Centre Ningbo University Sub-center, Ningbo, China

Funder

Natural Science Foundation of Ningbo

Healthy & Intelligent Kitchen Engineering Research Center of Zhejiang Province National Natural Science Foundation of China

K. C. Wong Magna Fund of Ningbo University

Humanities and Social Sciences Youth Foundation, Ministry of Education of China

Publisher

Informa UK Limited

Subject

Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2023.2175420

Reference35 articles.

1. A noise-immune Kalman filter for short-term traffic flow forecasting

2. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

3. Spatial visitation prediction of on-demand ride services using the scaling law

Cited by 6 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. Short-term traffic flow prediction based on vehicle trip chain features;Transportation Letters;2024-04-22

2. Enhanced forecasting of online car-hailing demand using an improved empirical mode decomposition with long short-term memory neural network;Transportation Letters;2024-02-08

3. Developing SAMM: A Model for Measuring Sustained Attention in Asynchronous Online Learning;Sustainability;2023-06-09

4. Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study;Frontiers in Psychology;2023-05-02

5. RF-BiLSTM Neural Network Incorporating Attention Mechanism for Online Ride-Hailing Demand Forecasting;Symmetry;2023-03-07

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