Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model

Li, Qin; Tan, Huachun; Jiang, Zhuxi; Wu, Yao; Ye, Linhui

doi:10.1016/j.neucom.2020.10.091

Cited by 12 publications

(4 citation statements)

References 43 publications

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“…In the traditional research of track big data congestion evaluation, in addition to the relatively mature research and application of linear road congestion evaluation and prediction, there are also travel characteristics at specific locations, OD travel analysis, taxi hotspot changes in a certain area based on GPS track data of taxis. Li [12] uses GPS data on weekdays to detect irregular traffic congestion. Badreddine [13] et al Detected road traffic congestion by establishing grid pairs.…”

Section: Introductionmentioning

confidence: 99%

Trajectory vector field calculation and visualization method for regional congestion evaluation

Zhang

Yao

et al. 2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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Congestion status evaluation is a vital link and tool in urban traffic congestion management. To timely warn and evaluate the congestion status of urban hot spots, a new congestion situation visualization model based on vector field is proposed. The calculation method of congestion status evaluation index is simplified based on this method. At the model building level, combining the advantages of OD flow diagram and OD matrix diagram, a dynamic visualization model of traffic congestion situation monitoring is established by constructing grid division and extracting action quantities. A method for identifying the trend of traffic congestion characteristics in urban areas is proposed. Simplify the algorithm level, combine the model visualization and the calculation process of the congestion evaluation index, and calculate the average actual travel time according to the vector field data structure, to realize the rapid calculation of the congestion index. The research shows that the model has the ability of trend analysis based on the visual expression of many basic traffic flow parameters such as flow, flow direction, speed, and spatial distribution, and realizes effective identification and early warning of hotspot congestion areas. The simplified calculation results of congestion index Compared with the traditional traffic index algorithm, the average error is 0.016. While the calculation efficiency is optimized, the effectiveness of the regional congestion index is guaranteed. This method can apply the research results to urban traffic congestion monitoring and management.

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Section: Introductionmentioning

confidence: 99%

Trajectory vector field calculation and visualization method for regional congestion evaluation

Zhang

Yao

et al. 2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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“…Trajectory prediction plays a crucial role in intelligent transportation systems (ITSs) as it helps autonomous vehicles perceive the current behavior of surrounding agents (SAs) and continuously predict their future actions to maintain efcient motion planning and navigation decisions, ensuring safety for all agents [1,2]. Additionally, trajectory prediction is benefcial for vehicle communication, vehicle control, and trafc safety and management, reducing high latency and data transmission interruptions in the vehicle network through early registration and resource allocation [3][4][5][6][7]. In L2 and L3 intelligent driving with mixed trafc fow, trajectory prediction is essential for maneuvering and potential collision warnings, as human driver intentions are often unavailable [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Ten, we combine the local and global interaction features to assist in prediction. (3) We conducted experiments on public datasets, and the experimental results of trajectory prediction show that the proposed model is superior to classical models.…”

Section: Introductionmentioning

confidence: 99%

TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction

Li,

Ou,

Liang

et al. 2023

Journal of Advanced Transportation

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Vehicle trajectory prediction can provide important support for intelligent transportation systems in areas such as autonomous driving, traffic control, and traffic flow optimization. Predicting vehicle trajectories is an extremely challenging task that not only depends on the vehicle’s historical trajectory but also on the dynamic and complex social-temporal relationships of the surrounding traffic network. The trajectory of the target vehicle is influenced by surrounding vehicles. However, existing methods have shortcomings in considering both time dependency and interactive dependency between vehicles or insufficient consideration of the impact of surrounding vehicles. To address this issue, we propose a hybrid deep learning model based on a temporal convolutional network (TCN) that considers local and global interactions between vehicles. Specifically, we use a social convolutional pooling layer to capture local interaction features between vehicles and a multihead self-attention layer to capture global interaction features between vehicles. Finally, we combine these two features using an encoder-decoder structure to predict vehicle trajectories. Through experiments on the Next-Generation Simulation (NGSIM) public dataset and ablation experiments, we validate the effectiveness of our model.

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“…Compared with urban traffic congestion, there are fewer studies that focus on traffic congestion in expressways. The existing studies are based on different data and methods to investigate traffic congestion in expressways [30][31][32][33][34][35][36][37][38][39][40] and analyze the characteristics of traffic congestion in expressways [41][42][43][44][45]. The summary of representative studies about traffic congestion in expressways is listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

A Method for Exploring and Analyzing Spatiotemporal Patterns of Traffic Congestion in Expressway Networks Based on Origin–Destination Data

Gao

Yan

et al. 2021

IJGI

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Traffic congestion in expressway networks has a strong negative influence on regional development. Understanding the spatiotemporal patterns of traffic congestion in expressway networks is critical for improving the exchange of products in regional production and promoting regional economic development. Nevertheless, existing studies pay less attention to these spatiotemporal patterns of traffic congestion. Considering that Origin–Destination (OD) data are available for the recorded spatial movements of vehicles in expressways, this study proposes a method with which to explore traffic congestion at the level of road segments in the regional expressway network, the mainstream of driving behaviors, and traffic regulations. Methods for analyzing spatial disparity and temporal changes in traffic congestion in expressway networks are also put forward in this paper. The empirical results show that the proposed methods could detect road segments where traffic congestion happens, and then uncover temporal patterns of several congested locations and spatial patterns of road segments with frequent congestion. These spatiotemporal patterns of traffic congestion could be in accord with the actual situation. This study provides a new approach to investigating traffic congestion in expressway networks based on low-cost data, which might be helpful for optimizing expressway network planning and promoting balanced regional development.

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Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model

Cited by 12 publications

References 43 publications

Trajectory vector field calculation and visualization method for regional congestion evaluation

Trajectory vector field calculation and visualization method for regional congestion evaluation

TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction

A Method for Exploring and Analyzing Spatiotemporal Patterns of Traffic Congestion in Expressway Networks Based on Origin–Destination Data

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