Learning Traffic Anomalies from Generative Models on Real-Time Observations

• URL: http://arxiv.org/abs/2502.01391v1
• Date: Mon, 03 Feb 2025 14:23:23 GMT
• Title: Learning Traffic Anomalies from Generative Models on Real-Time Observations
• Authors: Fotis I. Giasemis, Alexandros Sopasakis,
• Abstract summary: We use the Spatiotemporal Generative Adversarial Network (STGAN) framework to capture complex spatial and temporal dependencies in traffic data. We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020. Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates.
• Score: 49.1574468325115
• License:
• Abstract: Accurate detection of traffic anomalies is crucial for effective urban traffic management and congestion mitigation. We use the Spatiotemporal Generative Adversarial Network (STGAN) framework combining Graph Neural Networks and Long Short-Term Memory networks to capture complex spatial and temporal dependencies in traffic data. We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020. The images are processed to compute a flow metric representing vehicle density, which serves as input for the model. Training is conducted on data from April to November 2020, and validation is performed on a separate dataset from November 14 to 23, 2020. Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates. The detected anomalies include camera signal interruptions, visual artifacts, and extreme weather conditions affecting traffic flow.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing [0.6787248655856052]
  IncidentNet is a novel approach for classifying, localizing, and estimating the severity of traffic incidents. Our model works on microscopic traffic data that can be collected using cameras installed at traffic intersections.
  arXiv  Detail & Related papers  (2024-08-02T04:09:15Z)
• Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework [0.6554326244334868]
  State-of-the-art models often struggle to consider the data in the best way possible. We propose a novel framework to incorporate travel times between stations into a weighted adjacency matrix of a Graph Neural Network architecture.
  arXiv  Detail & Related papers  (2024-07-17T01:11:07Z)
• Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting [27.20703077756038]
  We propose Temporal Graph Learning Recurrent Neural Network (TGLRN) to address these problems. More precisely, to effectively model the nature of time series, we leverage Recurrent Neural Networks (RNNs) to dynamically construct a graph at each time step. Experimental results on four commonly used real-world benchmark datasets show the effectiveness of TGLRN.
  arXiv  Detail & Related papers  (2024-06-04T19:08:40Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction [0.40964539027092917]
  We propose STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2022-12-08T20:24:59Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network [16.56822335262946]
  We propose a novel deep learning model, Graph Convolutional Gated Recurrent Neural Network (GCGRNN), to predict network-wide, multi-step traffic volume. We have evaluated our model using two traffic datasets extracted from 150 sensors in Los Angeles, California, at the time resolutions one hour and 15 minutes, respectively.
  arXiv  Detail & Related papers  (2021-11-22T16:41:13Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.