SST-GCN: The Sequential based Spatio-Temporal Graph Convolutional networks for Minute-level and Road-level Traffic Accident Risk Prediction

• URL: http://arxiv.org/abs/2405.18602v2
• Date: Mon, 3 Jun 2024 08:44:05 GMT
• Title: SST-GCN: The Sequential based Spatio-Temporal Graph Convolutional networks for Minute-level and Road-level Traffic Accident Risk Prediction
• Authors: Tae-wook Kim, Han-jin Lee, Hyeon-Jin Jung, Ji-Woong Yang, Ellen J. Hong,
• Abstract summary: This paper proposes the Sequential based Spatio-Temporal Graph Convolutional Networks (SST-GCN) to predict traffic accidents at the Minute-Level and Road-Level. Experiments have demonstrated that SST-GCN outperforms other state-of-the-art models in Minute-Level predictions.
• Score: 1.2815904071470705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic accidents are recognized as a major social issue worldwide, causing numerous injuries and significant costs annually. Consequently, methods for predicting and preventing traffic accidents have been researched for many years. With advancements in the field of artificial intelligence, various studies have applied Machine Learning and Deep Learning techniques to traffic accident prediction. Modern traffic conditions change rapidly by the minute, and these changes vary significantly across different roads. In other words, the risk of traffic accidents changes minute by minute in various patterns for each road. Therefore, it is desirable to predict traffic accident risk at the Minute-Level and Road-Level. However, because roads have close and complex relationships with adjacent roads, research on predicting traffic accidents at the Minute-Level and Road-Level is challenging. Thus, it is essential to build a model that can reflect the spatial and temporal characteristics of roads for traffic accident prediction. Consequently, recent attempts have been made to use Graph Convolutional Networks to capture the spatial characteristics of roads and Recurrent Neural Networks to capture their temporal characteristics for predicting traffic accident risk. This paper proposes the Sequential based Spatio-Temporal Graph Convolutional Networks (SST-GCN), which combines GCN and LSTM, to predict traffic accidents at the Minute-Level and Road-Level using a road dataset constructed in Seoul, the capital of South Korea. Experiments have demonstrated that SST-GCN outperforms other state-of-the-art models in Minute-Level predictions.

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)
• Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge [2.6998782337240925]
  This paper presents a novel method that embeds real-time region-level knowledge using POIs, satellite images, and real-time access traces via LTE. It then ingests this embedded knowledge into a road-level attention-based prediction model. Experimental results on real-world road traffic prediction show that our model outperforms the baselines.
  arXiv  Detail & Related papers  (2024-08-23T07:34:26Z)
• ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction [47.17205142864036]
  ICST-DENT consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. ICST-DENT can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.
  arXiv  Detail & Related papers  (2024-04-22T03:35:19Z)
• 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)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• 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)
• A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction [16.28015945020806]
  We propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. Our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.
  arXiv  Detail & Related papers  (2022-05-30T16:24:43Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)
• 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)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework [12.279252772816216]
  Real-time traffic accident forecasting is increasingly important for public safety and urban management. Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. We propose a novel framework RiskOracle, to improve the prediction granularity to minute levels.
  arXiv  Detail & Related papers  (2020-02-19T07:18:46Z)

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.