A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction-Reference-Cited by-同舟云学术

A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction

Published:2021-09-21 Issue: Volume: Page:1-18
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Xu Xuecai¹,Jin Xiaofei²,Xiao Daiquan¹,Ma Changxi³,Wong S. C.⁴

Affiliation:

1. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

2. Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. (Wuhan Branch), Wuhan, China

3. School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou, China

4. Department of Transportation Engineering, University of Hong Kong Pokfulam Road, Hong Kong, China

Funder

National Natural Science Fund of China

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/10.1080/15472450.2021.1977639

Reference55 articles.

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

2. A hybrid ARFIMA and neural network model for electricity price prediction

3. Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

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