PPTNet: A Hybrid Periodic Pattern-Transformer Architecture for Traffic Flow Prediction and Congestion Identification

• URL: http://arxiv.org/abs/2505.13047v2
• Date: Fri, 13 Jun 2025 03:12:56 GMT
• Title: PPTNet: A Hybrid Periodic Pattern-Transformer Architecture for Traffic Flow Prediction and Congestion Identification
• Authors: Hongrui Kou, Jingkai Li, Ziyu Wang, Zhouhang Lv, Yuxin Zhang, Cheng Wang,
• Abstract summary: This paper proposes a Periodic Pattern Transformer Network (PPTNet) for traffic flow prediction.<n>High-precision traffic flow dataset is constructed based on drone aerial imagery data.<n>Transformer decoder dynamically models temporal dependencies, enabling accurate predictions of traffic density and speed.<n>Congestion probabilities are calculated in real-time using the predicted outcomes.
• Score: 9.49753674895755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of traffic flow parameters and real time identification of congestion states are essential for the efficient operation of intelligent transportation systems. This paper proposes a Periodic Pattern Transformer Network (PPTNet) for traffic flow prediction, integrating periodic pattern extraction with the Transformer architecture, coupled with a fuzzy inference method for real-time congestion identification. Firstly, a high-precision traffic flow dataset (Traffic Flow Dataset for China's Congested Highways and Expressways, TF4CHE) suitable for congested highway scenarios in China is constructed based on drone aerial imagery data. Subsequently, the proposed PPTNet employs Fast Fourier Transform to capture multi-scale periodic patterns and utilizes two-dimensional Inception convolutions to efficiently extract intra and inter periodic features. A Transformer decoder dynamically models temporal dependencies, enabling accurate predictions of traffic density and speed. Finally, congestion probabilities are calculated in real-time using the predicted outcomes via a Mamdani fuzzy inference-based congestion identification module. Experimental results demonstrate that the proposed PPTNet significantly outperforms mainstream traffic prediction methods in prediction accuracy, and the congestion identification module effectively identifies real-time road congestion states, verifying the superiority and practicality of the proposed method in real-world traffic scenarios. Project page: https://github.com/ADSafetyJointLab/PPTNet.

Related papers

• A Dynamic Transformer Network for Vehicle Detection [57.4144097001218]
  We present a dynamic Transformer network for vehicle detection (DTNet)<n>DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector.<n>To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection.
  arXiv  Detail & Related papers  (2025-06-03T11:29:35Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles [48.32593099620544]
  Traffic flow estimation (TFE) is crucial for intelligent transportation systems. This paper introduces a novel and cost-effective TFE framework that leverages sparse,temporal generative artificial intelligence (GAI) framework. Within this framework, the conditional encoder mines spatial-temporal correlations in the initial TFE results.
  arXiv  Detail & Related papers  (2024-07-10T20:26:04Z)
• 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)
• 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 Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework [1.181206257787103]
  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.
  arXiv  Detail & Related papers  (2022-12-14T12:39:47Z)
• 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)
• S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving [7.862992905548721]
  We propose S2TNet, which models the spatial-temporal interactions by the S-temporal Transformer and deals with the temporel sequences by temporal Transformer. The methods outperforms state-of-the-art methods on ApolloScape Trajectory dataset by more than 7% on both the weighted sum of Average and Final Displacement Error.
  arXiv  Detail & Related papers  (2022-06-22T08:12:31Z)
• 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)
• PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction [16.010576606023417]
  We propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective.
  arXiv  Detail & Related papers  (2020-03-16T05:36:58Z)

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.