Deep Learning-Driven Anomaly Detection for Intelligent Transportation Networks: A Multi-Modal Data Fusion Approach
N Priyadarshini - Tensorgate Journal of Sustainable …, 2024 - research.tensorgate.org
The proliferation of data from diverse sources such as sensors, cameras, and vehicle
telemetry has ushered in an era where Intelligent Transportation Networks (ITNs) can be …
telemetry has ushered in an era where Intelligent Transportation Networks (ITNs) can be …
[HTML][HTML] Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks
Anomaly detection over traffic data is crucial for transportation management and abnormal
behavior identification. An anomaly in real-world scenarios usually causes abnormal …
behavior identification. An anomaly in real-world scenarios usually causes abnormal …
Leveraging Deep Learning and Knowledge Distillation for Enhanced Traffic Anomaly Detection in Transportation Systems
This paper introduces an innovative approach to enhance traffic anomaly detection in
transportation systems using deep learning and knowledge distillation. We create a robust …
transportation systems using deep learning and knowledge distillation. We create a robust …
Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection
Traffic time series anomaly detection has been intensively studied for years because of its
potential applications in intelligent transportation. However, classical traffic anomaly …
potential applications in intelligent transportation. However, classical traffic anomaly …
[HTML][HTML] A framework for end-to-end deep learning-based anomaly detection in transportation networks
N Davis, G Raina, K Jagannathan - … research interdisciplinary perspectives, 2020 - Elsevier
We develop an end-to-end deep learning-based anomaly detection model for temporal data
in transportation networks. The proposed EVT-LSTM model is derived from the popular …
in transportation networks. The proposed EVT-LSTM model is derived from the popular …
Graph convolutional networks for traffic anomaly
Event detection has been an important task in transportation, whose task is to detect points
in time when large events disrupts a large portion of the urban traffic network. Travel …
in time when large events disrupts a large portion of the urban traffic network. Travel …
Graph convolutional adversarial networks for spatiotemporal anomaly detection
Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger
public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit …
public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit …
Automatic Traffic Anomaly Detection on the Road Network with Spatial‐Temporal Graph Neural Network Representation Learning
H Zhang, S Zhao, R Liu, W Wang… - … and Mobile Computing, 2022 - Wiley Online Library
Traffic anomaly detection is an essential part of an intelligent transportation system.
Automatic traffic anomaly detection can provide sufficient decision‐support information for …
Automatic traffic anomaly detection can provide sufficient decision‐support information for …
Anomaly detection in automated vehicles using multistage attention-based convolutional neural network
Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless
and real-time transfer of data, are imperative infrastructural advancements to realize the …
and real-time transfer of data, are imperative infrastructural advancements to realize the …
Competitive learning for unsupervised anomaly detection in intelligent transportation systems
Intelligent Transportation Systems (ITSs) are expected to have a profound impact on the
quality of experience in future smart cities. Anomaly detection is an imperative for urban ITS …
quality of experience in future smart cities. Anomaly detection is an imperative for urban ITS …