Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network

• URL: http://arxiv.org/abs/2111.11337v1
• Date: Mon, 22 Nov 2021 16:41:13 GMT
• Title: Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network
• Authors: Lei Lin, Weizi Li, Lei Zhu
• Abstract summary: We propose a novel deep learning model, Graph Convolutional Gated Recurrent Neural Network (GCGRNN), to predict network-wide, multi-step traffic volume. We have evaluated our model using two traffic datasets extracted from 150 sensors in Los Angeles, California, at the time resolutions one hour and 15 minutes, respectively.
• Score: 16.56822335262946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate prediction of network-wide traffic conditions is essential for intelligent transportation systems. In the last decade, machine learning techniques have been widely used for this task, resulting in state-of-the-art performance. We propose a novel deep learning model, Graph Convolutional Gated Recurrent Neural Network (GCGRNN), to predict network-wide, multi-step traffic volume. GCGRNN can automatically capture spatial correlations between traffic sensors and temporal dependencies in historical traffic data. We have evaluated our model using two traffic datasets extracted from 150 sensors in Los Angeles, California, at the time resolutions one hour and 15 minutes, respectively. The results show that our model outperforms the other five benchmark models in terms of prediction accuracy. For instance, our model reduces MAE by 25.3%, RMSE by 29.2%, and MAPE by 20.2%, compared to the state-of-the-art Diffusion Convolutional Recurrent Neural Network (DCRNN) model using the hourly dataset. Our model also achieves faster training than DCRNN by up to 52%. The data and implementation of GCGRNN can be found at https://github.com/leilin-research/GCGRNN.

Related papers

• A Multi-Graph Convolutional Neural Network Model for Short-Term Prediction of Turning Movements at Signalized Intersections [0.6215404942415159]
  This study introduces a novel deep learning architecture, referred to as the multigraph convolution neural network (MGCNN) for turning movement prediction at intersections. The proposed architecture combines a multigraph structure, built to model temporal variations in traffic data, with a spectral convolution operation to support modeling the spatial variations in traffic data over the graphs. The model's ability to perform short-term predictions over 1, 2, 3, 4, and 5 minutes into the future was evaluated against four baseline state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-02T05:41:25Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• GC-GRU-N for Traffic Prediction using Loop Detector Data [5.735035463793008]
  We use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space time. The model ranked second with the fastest inference time and a very close performance to first place (Transformers)
  arXiv  Detail & Related papers  (2022-11-13T06:32:28Z)
• Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction [6.968068088508505]
  We present a new sequence-to-sequence architecture for graph neural networks (GNNs) We also present a new benchmark benchmark dataset of street-level segment data in Montreal, Canada. Our model improves performance by more than 7% for one-hour prediction compared to the baseline methods.
  arXiv  Detail & Related papers  (2022-09-08T14:56:29Z)
• Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting [12.223433627287605]
  We propose a new Spatial-temporal forecasting model, namely the Residual Graph Convolutional Recurrent Network (RGCRN) The model uses our proposed Residual Graph Convolutional Network (ResGCN) to capture the fine-grained spatial correlation of the traffic road network. Our comparative experimental results on two real datasets show that RGCRN improves on average by 20.66% compared to the best baseline model.
  arXiv  Detail & Related papers  (2022-05-03T13:23:38Z)
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining [58.10436813430554]
  Mini-batch training of graph neural networks (GNNs) requires a lot of computation and data movement. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised.
  arXiv  Detail & Related papers  (2021-10-16T02:41:35Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Predicting traffic signals on transportation networks using spatio-temporal correlations on graphs [56.48498624951417]
  This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
  arXiv  Detail & Related papers  (2021-04-27T18:17:42Z)
• Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction [12.550067622364162]
  This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a road network. The SRNN model trained with data of a road network is able to predict traffic speed of different road networks, with the fixed number of parameters to train.
  arXiv  Detail & Related papers  (2021-03-03T18:21:49Z)
• 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)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.