DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

• URL: http://arxiv.org/abs/1709.09585v4
• Date: Sun, 20 Aug 2023 02:36:27 GMT
• Title: DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
• Authors: Xingyi Cheng, Ruiqing Zhang, Jie Zhou, Wei Xu
• Abstract summary: 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.
• Score: 31.65583737358249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to a lack of mining road topology. To address the effect attenuation problem, we suggest taking into account the traffic of surrounding locations(wider than the adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, an attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with a 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in the temporal and spatial domains. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.

Related papers

• 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)
• 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)
• 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)
• STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting [8.596556653895028]
  This study proposes a new spatial-temporal neural network architecture to handle the long-term traffic parameters forecasting issue. The attention mechanism potentially guarantees long-term prediction performance without significant information loss from distant inputs.
  arXiv  Detail & Related papers  (2022-10-01T05:58:22Z)
• STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction [12.223433627287605]
  Traffic data are highly nonlinear and have complex spatial correlations between road nodes. Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations. We propose a new prediction model which captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data.
  arXiv  Detail & Related papers  (2022-03-21T06:38:34Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting [11.495992519252585]
  We argue that such correlations are universal and play a pivotal role in traffic flow. We propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor. The proposed STNN model can be applied on any unseen traffic networks.
  arXiv  Detail & Related papers  (2021-09-11T09:04:35Z)
• Spatial-Temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction [17.94199362114272]
  In intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation. We propose the Spatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal block uses unidirectional convolution to effectively capture short-term periodic temporal dependence. We conduct extensive experiments on large-scale real-world tasks and verify the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2021-05-21T17:43:07Z)
• 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)

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.