A novel image-based convolutional neural network approach for traffic congestion estimation
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A novel image-based convolutional neural network approach for traffic congestion estimation
Authors
Keywords
Traffic congestion, Convolutional neural network, Vehicle detection, Deep learning, Traffic flow parameter
Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 180, Issue -, Pages 115037
Publisher
Elsevier BV
Online
2021-05-02
DOI
10.1016/j.eswa.2021.115037
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework
- (2021) Jinlong Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors
- (2019) Junxuan Zhao et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A deep learning approach for quality enhancement of surveillance video
- (2019) Dandan Ding et al. Journal of Intelligent Transportation Systems
- Object Detection With Deep Learning: A Review
- (2019) Zhong-Qiu Zhao et al. IEEE Transactions on Neural Networks and Learning Systems
- Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network
- (2018) Jiyong Chung et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow
- (2018) Ruimin Ke et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Locality constraint distance metric learning for traffic congestion detection
- (2018) Qi Wang et al. PATTERN RECOGNITION
- Vehicle classification from low-frequency GPS data with recurrent neural networks
- (2018) Matteo Simoncini et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Management of traffic congestion in adaptive traffic signals using a novel classification-based approach
- (2018) Ali Sadollah et al. ENGINEERING OPTIMIZATION
- Automated Traffic Surveillance System with Aerial Camera Arrays Imagery: Macroscopic Data Collection with Vehicle Tracking
- (2017) Xi Zhao et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Urban traffic congestion estimation and prediction based on floating car trajectory data
- (2016) Xiangjie Kong et al. Future Generation Computer Systems-The International Journal of eScience
- Congested scene classification via efficient unsupervised feature learning and density estimation
- (2016) Yuan Yuan et al. PATTERN RECOGNITION
- Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model
- (2015) Yao-Jan Wu et al. Journal of Intelligent Transportation Systems
- Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
- (2015) Xiaolei Ma et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition
- (2014) Jun-Wei Hsieh et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Deploying a network of smart cameras for traffic monitoring on a “city kernel”
- (2013) Luca Calderoni et al. EXPERT SYSTEMS WITH APPLICATIONS
- Video-based traffic data collection system for multiple vehicle types
- (2013) Shuguang Li et al. IET Intelligent Transport Systems
- Length-based vehicle classification using event-based loop detector data
- (2013) Henry X. Liu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Vehicle detection and tracking in airborne videos by multi-motion layer analysis
- (2011) Xianbin Cao et al. MACHINE VISION AND APPLICATIONS
- An algorithm for the recognition of levels of congestion in road traffic problems
- (2007) Angélica Lozano et al. MATHEMATICS AND COMPUTERS IN SIMULATION
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started