Multi-modal generative adversarial networks for traffic event detection in smart cities
Advances in the Internet of Things have enabled the development of many smart city
applications and expert systems that help citizens and authorities better understand the …
applications and expert systems that help citizens and authorities better understand the …
Automated traffic incident detection with a smaller dataset based on generative adversarial networks
An imbalanced and small training sample can cause an incident detection model to have a
low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a …
low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a …
Traffic accident data generation based on improved generative adversarial networks
Z Chen, J Zhang, Y Zhang, Z Huang - Sensors, 2021 - mdpi.com
For urban traffic, traffic accidents are the most direct and serious risk to people's lives, and
rapid recognition and warning of traffic accidents is an important remedy to reduce their …
rapid recognition and warning of traffic accidents is an important remedy to reduce their …
Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance
P Devadhas Sujakumari, P Dassan - Symmetry, 2023 - mdpi.com
The most crucial component of any smart city traffic management system is traffic flow
prediction. It can assist a driver in selecting the most efficient route to their destination. The …
prediction. It can assist a driver in selecting the most efficient route to their destination. The …
Real-time traffic incident detection based on a hybrid deep learning model
Small sample sizes and imbalanced datasets have been two difficulties in previous traffic
incident detection-related studies. Moreover, real-time characteristics of incident detection …
incident detection-related studies. Moreover, real-time characteristics of incident detection …
Inferring intersection traffic patterns with sparse video surveillance information: An st-gan method
Traffic patterns of urban road intersections are important in traffic monitoring and accident
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …
prediction, thus play crucial roles in urban traffic management. Although real-time traffic …
A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing
Forecasting traffic accidents at a fine-grained spatial scale is essential to provide effective
precautions and improve traffic safety in smart urban sensing applications. Current solutions …
precautions and improve traffic safety in smart urban sensing applications. Current solutions …
How generative adversarial networks promote the development of intelligent transportation systems: A survey
In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges
With the emerging concepts of smart cities and intelligent transportation systems, accurate
traffic sensing and prediction have become critically important to support urban …
traffic sensing and prediction have become critically important to support urban …
Smart city transportation: Deep learning ensemble approach for traffic accident detection
VA Adewopo, N Elsayed - IEEE Access, 2024 - ieeexplore.ieee.org
The dynamic and unpredictable nature of road traffic necessitates effective accident
detection methods for enhancing safety and streamlining traffic management in smart cities …
detection methods for enhancing safety and streamlining traffic management in smart cities …