Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis

• URL: http://arxiv.org/abs/2411.02542v1
• Date: Mon, 04 Nov 2024 19:22:33 GMT
• Title: Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis
• Authors: Xiwen Chen, Sayed Pedram Haeri Boroujeni, Xin Shu, Huayu Li, Abolfazl Razi,
• Abstract summary: We propose the Concurrency Prior (CP) method to enhance the predictive capabilities of general Graph Neural Network (GNN) models in traffic incident prediction tasks. Our method allows GNNs to incorporate concurrent incident information, as mentioned in the hypothesis, via tokenization with negligible extra parameters. The extensive experiments, utilizing real-world data across states and cities in the USA, demonstrate that integrating CP into 12 state-of-the-art GNN architectures leads to significant improvements.
• Score: 2.3379022809760763
• License:
• Abstract: Despite recent progress in reducing road fatalities, the persistently high rate of traffic-related deaths highlights the necessity for improved safety interventions. Leveraging large-scale graph-based nationwide road network data across 49 states in the USA, our study first posits the Concurrency Hypothesis from intuitive observations, suggesting a significant likelihood of incidents occurring at neighboring nodes within the road network. To quantify this phenomenon, we introduce two novel metrics, Average Neighbor Crash Density (ANCD) and Average Neighbor Crash Continuity (ANCC), and subsequently employ them in statistical tests to validate the hypothesis rigorously. Building upon this foundation, we propose the Concurrency Prior (CP) method, a powerful approach designed to enhance the predictive capabilities of general Graph Neural Network (GNN) models in semi-supervised traffic incident prediction tasks. Our method allows GNNs to incorporate concurrent incident information, as mentioned in the hypothesis, via tokenization with negligible extra parameters. The extensive experiments, utilizing real-world data across states and cities in the USA, demonstrate that integrating CP into 12 state-of-the-art GNN architectures leads to significant improvements, with gains ranging from 3% to 13% in F1 score and 1.3% to 9% in AUC metrics. The code is publicly available at https://github.com/xiwenc1/Incident-GNN-CP.

Related papers

• Geographical Information Alignment Boosts Traffic Analysis via Transpose Cross-attention [4.323740171581589]
  We propose a plug-in-and-play module for common GNN frameworks, termed Geographic Information Alignment (GIA) This module can efficiently fuse the node feature and geographic position information through a novel Transpose Cross-attention mechanism. Our method can obtain gains ranging from 1.3% to 10.9% in F1 score and 0.3% to 4.8% in AUC.
  arXiv  Detail & Related papers  (2024-12-03T21:04:49Z)
• Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks [50.87615167799367]
  We certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph. Our framework provides fundamental insights into the role of graph structure and its connectivity on the worst-case behavior of convolution-based and PageRank-based GNNs.
  arXiv  Detail & Related papers  (2024-07-15T16:12:51Z)
• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction [6.570852598591727]
  We introduce the Stemporal Zero-Inflated Tweedie Graph Neural Network STZITZTDGNN -- the first uncertainty-aware graph deep learning model in road traffic accident prediction for multisteps. Our study demonstrates that STIDGNN can effectively inform targeted road monitoring, thereby improving urban road safety strategies.
  arXiv  Detail & Related papers  (2023-09-10T16:35:47Z)
• Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction [15.248412426672694]
  Graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature. In this work, we classify the components of successful GCNN prediction models and analyze the effects of factorization, attention mechanism, and weight sharing on their performance.
  arXiv  Detail & Related papers  (2023-05-30T00:50:51Z)
• Uncertainty Quantification over Graph with Conformalized Graph Neural Networks [52.20904874696597]
  Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-23T21:38:23Z)
• TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks [36.975060335456035]
  Existing machine learning approaches tend to focus on predicting traffic accidents in isolation. To incorporate graph structure information, Graph Neural Networks (GNNs) can be naturally applied. Applying GNNs to the accident prediction problem faces challenges due to the lack of suitable graph-structured traffic accident datasets.
  arXiv  Detail & Related papers  (2023-04-17T22:18:58Z)
• Unveiling the potential of Graph Neural Networks for robust Intrusion Detection [2.21481607673149]
  We propose a novel Graph Neural Network (GNN) model to learn flow patterns of attacks structured as graphs. Our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under adversarial attacks.
  arXiv  Detail & Related papers  (2021-07-30T16:56:39Z)
• A Biased Graph Neural Network Sampler with Near-Optimal Regret [57.70126763759996]
  Graph neural networks (GNN) have emerged as a vehicle for applying deep network architectures to graph and relational data. In this paper, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem. We introduce a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded payouts.
  arXiv  Detail & Related papers  (2021-03-01T15:55:58Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

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.