HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data

• URL: http://arxiv.org/abs/2203.03100v1
• Date: Mon, 7 Mar 2022 02:11:04 GMT
• Title: HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
• Authors: Bang An, Amin Vahedian, Xun Zhou, W. Nick Street, Yanhua Li
• Abstract summary: Traffic accident forecasting is a significant problem for transportation management and public safety. The occurrence of traffic accidents is affected by complex dependencies among spatial and temporal features. Recent traffic accident prediction methods have attempted to use deep learning models to improve accuracy.
• Score: 17.345649325770957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic accident forecasting is a significant problem for transportation management and public safety. However, this problem is challenging due to the spatial heterogeneity of the environment and the sparsity of accidents in space and time. The occurrence of traffic accidents is affected by complex dependencies among spatial and temporal features. Recent traffic accident prediction methods have attempted to use deep learning models to improve accuracy. However, most of these methods either focus on small-scale and homogeneous areas such as populous cities or simply use sliding-window-based ensemble methods, which are inadequate to handle heterogeneity in large regions. To address these limitations, this paper proposes a novel Hierarchical Knowledge Transfer Network (HintNet) model to better capture irregular heterogeneity patterns. HintNet performs a multi-level spatial partitioning to separate sub-regions with different risks and learns a deep network model for each level using spatio-temporal and graph convolutions. Through knowledge transfer across levels, HintNet archives both higher accuracy and higher training efficiency. Extensive experiments on a real-world accident dataset from the state of Iowa demonstrate that HintNet outperforms the state-of-the-art methods on spatially heterogeneous and large-scale areas.

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)
• Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity [18.566139471849844]
  Traffic accidents pose a significant risk to human health and property safety. To prevent traffic accidents, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents.
  arXiv  Detail & Related papers  (2024-07-29T03:10:15Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
  Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems. We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model. For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
  arXiv  Detail & Related papers  (2023-06-09T21:01:29Z)
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning [112.69497636932955]
  Federated learning aims to train models across different clients without the sharing of data for privacy considerations. We study how data heterogeneity affects the representations of the globally aggregated models. We propose sc FedDecorr, a novel method that can effectively mitigate dimensional collapse in federated learning.
  arXiv  Detail & Related papers  (2022-10-01T09:04:17Z)
• Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems [0.7340017786387767]
  In this paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches.
  arXiv  Detail & Related papers  (2021-06-15T17:33:36Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• 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)
• DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting [31.65583737358249]
  We propose an end-to-end framework called DeepTransport to predict traffic conditions. CNN and RNN are utilized to obtain spatial-temporal traffic information within a transport network topology. We constructed and released a real-world large traffic condition dataset with a 5-minute resolution.
  arXiv  Detail & Related papers  (2017-09-27T15:39:49Z)

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.