Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones
Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecastin...
Saved in:
Main Authors: | Shahriar Afandizadeh, Saeid Abdolahi, Hamid Mirzahossein |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-01-01
|
Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2024/9981657 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Structural Comparison between the Origin-Destination Matrices Based on Local Windows with Socioeconomic, Land-Use, and Population Characteristics
by: Shahriar Afandizadeh Zargari, et al.
Published: (2021-01-01) -
A parallel spatiotemporal deep learning network for highway traffic flow forecasting
by: Dongxiao Han, et al.
Published: (2019-02-01) -
Research and application of traffic engineering algorithm based on deep learning
by: Daoyun HU, et al.
Published: (2021-02-01) -
One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model
by: Sujin Lee, et al.
Published: (2021-01-01) -
Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting
by: Chaoyu Ren, et al.
Published: (2024-01-01)