Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features

• URL: http://arxiv.org/abs/2311.17256v1
• Date: Tue, 28 Nov 2023 22:33:22 GMT
• Title: Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features
• Authors: Tin T. Nguyen, Simeon C. Calvert, Guopeng Li, Hans van Lint
• Abstract summary: This paper proposes a content-based retrieval system for traffic patterns of highway traffic congestion. To effectively interpret retrieval outcomes, the paper proposes a graph-based approach (relation-graph) for the former component. In the latter component, the similarities between congestion patterns are customizable according to user expectations.
• Score: 5.32051334268552
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The fast-growing amount of traffic data brings many opportunities for revealing more insightful information about traffic dynamics. However, it also demands an effective database management system in which information retrieval is arguably an important feature. The ability to locate similar patterns in big datasets potentially paves the way for further valuable analyses in traffic management. This paper proposes a content-based retrieval system for spatiotemporal patterns of highway traffic congestion. There are two main components in our framework, namely pattern representation and similarity measurement. To effectively interpret retrieval outcomes, the paper proposes a graph-based approach (relation-graph) for the former component, in which fundamental traffic phenomena are encoded as nodes and their spatiotemporal relationships as edges. In the latter component, the similarities between congestion patterns are customizable with various aspects according to user expectations. We evaluated the proposed framework by applying it to a dataset of hundreds of patterns with various complexities (temporally and spatially). The example queries indicate the effectiveness of the proposed method, i.e. the obtained patterns present similar traffic phenomena as in the given examples. In addition, the success of the proposed approach directly derives a new opportunity for semantic retrieval, in which expected patterns are described by adopting the relation-graph notion to associate fundamental traffic phenomena.

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)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting [35.0288931087826]
  Traffic flow forecasting aims to predict future traffic conditions on the basis of networks and traffic conditions in the past. The problem is typically solved by modeling complex-temporal correlations in traffic data using far-temporal neural networks (GNNs) Existing methods follow the paradigm of message passing that aggregates neighborhood information linearly. In this paper, we propose a model named Dynamic Hyper Structure Learning (DyHSL) for traffic flow prediction.
  arXiv  Detail & Related papers  (2023-09-21T12:44:55Z)
• RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments [72.04891523115535]
  We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
  arXiv  Detail & Related papers  (2022-07-16T12:40:17Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction [12.223433627287605]
  Traffic data are highly nonlinear and have complex spatial correlations between road nodes. Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations. We propose a new prediction model which captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data.
  arXiv  Detail & Related papers  (2022-03-21T06:38:34Z)
• On the Design of Graph Embeddings for the Sensorless Estimation of Road Traffic Profiles [6.100274095771616]
  Traffic forecasting models rely on data that needs to be sensed, processed, and stored. The lack of sensed locations can be complemented with synthetic data simulations that further lower the economical investment needed for traffic monitoring. We present a method to discover locations among those with available traffic data by inspecting topological features of road segments.
  arXiv  Detail & Related papers  (2022-01-11T15:16:18Z)
• 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)
• Bayesian Graph Convolutional Network for Traffic Prediction [23.30484840210517]
  We propose a Bayesian Graph Convolutional Network (BGCN) framework to alleviate these issues. Under this framework, the graph structure is viewed as a random realization from a parametric generative model. We verify the effectiveness of our method on five real-world datasets, and the experimental results demonstrate that BGCN attains superior performance compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-01T14:19:37Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• Large-scale Analysis and Simulation of Traffic Flow using Markov Models [0.0]
  A mathematically rigorous model that can be used for traffic analysis was proposed earlier by other researchers. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory.
  arXiv  Detail & Related papers  (2020-07-06T12:31:27Z)

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.