Traffic congestion anomaly detection and prediction using deep learning

• URL: http://arxiv.org/abs/2006.13215v1
• Date: Tue, 23 Jun 2020 08:49:46 GMT
• Title: Traffic congestion anomaly detection and prediction using deep learning
• Authors: Adriana-Simona Mihaita, Haowen Li, Marian-Andrei Rizoiu
• Abstract summary: Congestion prediction is a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, but this is a challenging task due to inter-dependencies of traffic flow both in time and space. We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.
• Score: 6.370406399003785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however, this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however, open questions remain around their applicability, accuracy and parameter tuning. This paper brings two contributions in terms of: 1) applying an outlier detection an anomaly adjustment method based on incoming and historical data streams, and 2) proposing an advanced deep learning framework for simultaneously predicting the traffic flow, speed and occupancy on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future. Lastly, we prove that the anomaly adjustment method brings significant improvements to using deep learning in both time and space.

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)
• Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting [13.770733370640565]
  This paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems. We propose an Adaptive Double Correction by Series Decomposition (ADCSD) method, which first decomposes the output of the trained model into seasonal and trend-cyclical parts. In the proposed ADCSD method, instead of fine-tuning the whole trained model during the testing phase, a lite network is attached after the trained model, and only the lite network is fine-tuned in the testing process each time a data entry is observed.
  arXiv  Detail & Related papers  (2024-01-08T12:04:39Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction [3.8277254030074537]
  Deep-temporal learning method is proposed to predict daily traffic volume in three phases. In decision phase, traffic volumes on a coming day network-wide toll stations would be achieved effectively.
  arXiv  Detail & Related papers  (2023-08-11T14:33:20Z)
• 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)
• 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)
• 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)
• Graph modelling approaches for motorway traffic flow prediction [6.370406399003785]
  This paper presents two new spatial-temporal approaches for building accurate short-term prediction along a popular motorway in Sydney. The methods are built on proximity-based approaches, denoted backtracking and proximity, which uses the most recent and closest traffic flow information for each of the target counting stations along the motorway. The results indicate that for short-term predictions (less than 10 minutes into the future), the proposed graph-based approaches outperform state-of-the-art deep learning models.
  arXiv  Detail & Related papers  (2020-06-26T06:54:14Z)
• An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction [0.22940141855172028]
  The proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets.
  arXiv  Detail & Related papers  (2020-05-08T14:23:52Z)
• 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.