Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads

• URL: http://arxiv.org/abs/2412.11095v1
• Date: Sun, 15 Dec 2024 07:30:01 GMT
• Title: Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads
• Authors: Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka,
• Abstract summary: We propose a structured framework for simultaneous modeling travel time distributions in both directions along arterial corridors. The results demonstrate resilience to effective traffic variations, including cycle lengths, green percentages, traffic density, and counterfactual routes. This framework supports dynamic signal timing, enhances congestion management, and improves travel time reliability in real-world applications.
• Score: 5.849150965368483
• License:
• Abstract: Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric. Traditional approaches, such as Coordinated Signal Timing and Adaptive Traffic Control Systems, often lack scalability and generalizability across diverse urban layouts. We propose Fusion-based Dynamic Graph Neural Networks (FDGNN), a structured framework for simultaneous modeling of travel time distributions in both directions along arterial corridors. FDGNN utilizes attentional graph convolution on dynamic, bidirectional graphs and integrates fusion techniques to capture evolving spatiotemporal traffic dynamics. The framework is trained on extensive hours of simulation data and utilizes GPU computation to ensure scalability. The results demonstrate that our framework can efficiently and accurately model travel time as a normal distribution on arterial roads leveraging a unique dynamic graph representation of corridor traffic states. This representation integrates sequential traffic signal timing plans, local driving behaviors, temporal turning movement counts, and ingress traffic volumes, even when aggregated over intervals as short as a single cycle length. The results demonstrate resilience to effective traffic variations, including cycle lengths, green time percentages, traffic density, and counterfactual routes. Results further confirm its stability under varying conditions at different intersections. This framework supports dynamic signal timing, enhances congestion management, and improves travel time reliability in real-world applications.

Related papers

• 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)
• Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting [27.20703077756038]
  We propose Temporal Graph Learning Recurrent Neural Network (TGLRN) to address these problems. More precisely, to effectively model the nature of time series, we leverage Recurrent Neural Networks (RNNs) to dynamically construct a graph at each time step. Experimental results on four commonly used real-world benchmark datasets show the effectiveness of TGLRN.
  arXiv  Detail & Related papers  (2024-06-04T19:08:40Z)
• Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation [0.6650227510403052]
  Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADG-N) is proposed to improve traffic flow prediction in highway transportation. A dedicated gated kernel emphasizing highly relative nodes is introduced on complete graphs to reduce overfitting for graph convolution operations.
  arXiv  Detail & Related papers  (2023-09-13T13:57:21Z)
• 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)
• STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction [0.40964539027092917]
  We propose STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2022-12-08T20:24:59Z)
• 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)
• D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights [68.76631399516823]
  We present a trajectory prediction approach with respect to traffic lights, D2-TPred, using a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) Our experimental results show that our model achieves more than 20.45% and 20.78% in terms of ADE and FDE, respectively, on VTP-TL.
  arXiv  Detail & Related papers  (2022-07-21T10:19:07Z)
• Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting [1.52292571922932]
  We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) for traffic forecasting in this paper. In STIDGCN, we propose an interactive dynamic graph convolution structure, which first divides the sequences at intervals and captures the spatial-temporal dependence of the traffic data simultaneously. Experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
  arXiv  Detail & Related papers  (2022-05-18T01:59:30Z)
• TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting [41.87633457352356]
  This paper proposes a neural network model that focuses on the globality and locality of traffic networks. Experiments on two real-world datasets show that the model can scrutinize the spatial-temporal correlation of traffic data.
  arXiv  Detail & Related papers  (2020-11-30T09:21:43Z)
• 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)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.