Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework

• URL: http://arxiv.org/abs/2212.07194v1
• Date: Wed, 14 Dec 2022 12:39:47 GMT
• Title: Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework
• Authors: Jianlei Kong, Xiaomeng Fan, Xue-Bo Jin, and Min Zuo
• Abstract summary: This paper proposes a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is constructed by combining variational inference with gated recurrent units (GRU) and used as the deep neural network unit of the encoder-decoder framework. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks.
• Score: 1.181206257787103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for mastering traffic and making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Furthermore, the sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. This paper proposes a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is constructed by combining variational inference with gated recurrent units (GRU) and used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.

Related papers

• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• A Multi-Channel Spatial-Temporal Transformer Model for Traffic Flow Forecasting [0.0]
  We propose a multi-channel spatial-temporal transformer model for traffic flow forecasting. It improves the accuracy of the prediction by fusing results from different channels of traffic data. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance.
  arXiv  Detail & Related papers  (2024-05-10T06:37:07Z)
• 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 Quantification for Image-based Traffic Prediction across Cities [63.136794104678025]
  Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning. We investigate their application to a large-scale image-based traffic dataset spanning multiple cities. We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
  arXiv  Detail & Related papers  (2023-08-11T13:35:52Z)
• A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting [21.6456624219159]
  A crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles. We propose a slow shifting concerned machine learning method for traffic flow forecasting, which includes two parts. Our proposed method outperforms the state-of-art results by 14.55% and 62.56% using the metrics of root mean squared error and mean absolute percentage error, respectively.
  arXiv  Detail & Related papers  (2023-03-31T03:07:53Z)
• TraffNet: Learning Causality of Traffic Generation for What-if Prediction [4.604622556490027]
  Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction.
  arXiv  Detail & Related papers  (2023-03-28T13:12:17Z)
• Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU [5.320087179174425]
  We develop non-linear memory-based deep neural network models to forecast traffic volume in real-time. Our experiments demonstrate the effectiveness of the proposed models in predicting traffic volume in highly dynamic and heterogeneous traffic environments.
  arXiv  Detail & Related papers  (2023-03-22T15:25:07Z)
• 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)
• 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)
• 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)

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.