Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial...
Saved in:
Main Authors: | Abdulrahman Alruban, Hanan Abdullah Mengash, Majdy M. Eltahir, Nabil Sharaf Almalki, Ahmed Mahmud, Mohammed Assiri |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10379096/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Corrections to “Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems”
by: Abdulrahman Alruban, et al.
Published: (2025-01-01) -
Avoidance Traffic Congestion Through Smart Transportation Approach
by: Chenguang Wang
Published: (2022-12-01) -
Investigating the Effects of Optimal Use of Public Transport to Reduce Traffic and Air Pollution in Tabriz
by: Hamed Ahmadzadeh, et al.
Published: (2023-03-01) -
ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment
by: Marco Guerrieri, et al.
Published: (2025-01-01) -
INFLUENCE OF PSYCHOPHYSIOLOGICAL FACTORS OF ETHICAL RELATIONSHIP OF DRIVERS ON THE EFFICIENCY OF THE FREIGHT TRANSPORTATION IN INTERNATIONAL TRAFFIC
by: О. А. Tettsoeva
Published: (2019-12-01)