Predicting traffic incident duration using deep learning model with real-time data
Traffic accidents have a negative impact on traffic. The prediction of incident clearance time helps to reduce its impact by diverting traffic flow, assisting traffic management organizations in making decisions about appropriate responses and resource allocation and identifying critical factors tha...
Saved in:
Main Author: | Zhang, Ruilin |
---|---|
Other Authors: | Zhu Feng |
Format: | Final Year Project |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/77626 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Deep learning-based traffic flow prediction and traffic management system for urban transportation networks
by: Zhao, Han
Published: (2023) -
Analysis and prediction of traffic congestion and incident duration in a road network
by: Kalyanaraman Manikandan Jananni
Published: (2018) -
Predicting the duration and impact of the non-recurring road incidents on the transportation network
by: Ghosh, Banishree
Published: (2019) -
A hybrid deep learning approach for real-time estimation of passenger traffic flow in urban railway systems
by: Fu, Xianlei, et al.
Published: (2023) -
Developing real-time traffic prediction with deep neural networks
by: Zhou, Tianchen
Published: (2023)