MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks

• URL: http://arxiv.org/abs/2501.12281v1
• Date: Tue, 21 Jan 2025 16:52:42 GMT
• Title: MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks
• Authors: Qishen Zhou, Yifan Zhang, Michail A. Makridis, Anastasios Kouvelas, Yibing Wang, Simon Hu,
• Abstract summary: MoGERNN can accurately predict congestion evolution even in areas without sensors, offering valuable information for traffic management. Experiments on two real-world datasets show MoGERNN consistently outperforms baseline methods for both observed and unobserved locations.
• Score: 15.487715528848456
• License:
• Abstract: Given a partially observed road network, how can we predict the traffic state of unobserved locations? While deep learning approaches show exceptional performance in traffic prediction, most assume sensors at all locations of interest, which is impractical due to financial constraints. Furthermore, these methods typically require costly retraining when sensor configurations change. We propose MoGERNN, an inductive spatio-temporal graph representation model, to address these challenges. Inspired by the Mixture of Experts approach in Large Language Models, we introduce a Mixture of Graph Expert (MoGE) block to model complex spatial dependencies through multiple graph message aggregators and a sparse gating network. This block estimates initial states for unobserved locations, which are then processed by a GRU-based Encoder-Decoder that integrates a graph message aggregator to capture spatio-temporal dependencies and predict future states. Experiments on two real-world datasets show MoGERNN consistently outperforms baseline methods for both observed and unobserved locations. MoGERNN can accurately predict congestion evolution even in areas without sensors, offering valuable information for traffic management. Moreover, MoGERNN is adaptable to dynamic sensing networks, maintaining competitive performance even compared to its retrained counterpart. Tests with different numbers of available sensors confirm its consistent superiority, and ablation studies validate the effectiveness of its key modules.

Related papers

• 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. 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)
• 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)
• Newell's theory based feature transformations for spatio-temporal traffic prediction [0.0]
  We propose a traffic flow physics-based transformation feature for Deep learning (DL) models for traffic flow forecasting. This transformation incorporates Newell's uncongested and congested filters of traffic flows at the target locations, enabling the models to learn broader dynamics of the system. An important advantage of our framework is its ability to be transferred to new locations where data is unavailable.
  arXiv  Detail & Related papers  (2023-07-12T06:31:43Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Graph Convolutional Networks for Traffic Forecasting with Missing Values [0.5774786149181392]
  We propose a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.
  arXiv  Detail & Related papers  (2022-12-13T08:04:38Z)
• Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks [1.0965065178451106]
  We develop a framework that allows forecasting the state at entirely unobserved locations based on spatial-temporal correlations and the graph inductive bias. Our framework can be combined with any Graph Neural Network, that exploits surrounding correlations with observed locations by using the network's graph structure. Our empirical evaluation of both simulated and real-world datasets demonstrates that Graph Neural Networks are well-suited for this task.
  arXiv  Detail & Related papers  (2022-11-21T15:52:06Z)
• 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)
• Few-Shot Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases [7.173242326298134]
  In many cities, the available amount of traffic data is substantially below the minimum requirement due to the data collection expense. This paper develops a graph network (GN)-based deep learning model LocaleGn that depicts the traffic dynamics using localized data. It is also demonstrated that the learned knowledge from LocaleGn can be transferred across cities.
  arXiv  Detail & Related papers  (2022-03-08T09:46:50Z)
• DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction [4.302265301004301]
  Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion. utilizing these data presents unique challenges including: the dynamic temporal correlation, and the dynamic spatial correlation caused by changes in road conditions. We propose DetectorNet enhanced by Transformer to address these challenges.
  arXiv  Detail & Related papers  (2021-10-19T03:47:38Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
  In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
  arXiv  Detail & Related papers  (2020-07-23T14:31:25Z)

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.