A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow
Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-t...
Main Authors: | Zoe Bartlett, Liangxiu Han, Trung Thanh Nguyen, Princy Johnson |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8846033/ |
Similar Items
-
Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification
by: Angela María Vargas Arcila, et al.
Published: (2021-03-01) -
Patterns of traffic congestion indicator at some intersections of the road network
by: M. G. Boyarshinov, et al.
Published: (2024-02-01) -
Artificial intelligence-based traffic flow prediction: a comprehensive review
by: Sayed A. Sayed, et al.
Published: (2023-03-01) -
Road congestion pricing in Europe : implications for the United States /
by: Richardson, Harry Ward, et al.
Published: (2008) -
IDENTIFICATION OF TRAFFIC CONGESTION ON URBAN ARTERIALS FOR HETEROGENEOUS TRAFFIC
by: Amudapuram Mohan RAO, et al.
Published: (2016-09-01)