Traffic congestion propagation inference using dynamic Bayesian graph convolution network

Luan, Sen; Ke, Ruimin; Huang, Zhou; Ma, Xiaolei

doi:10.1016/j.trc.2021.103526

Cited by 45 publications

(13 citation statements)

References 24 publications

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“…In the experiment, we choose the batch size as [16,32,64], the learning rate as [0.001, 0.005, 0.01, 0.02], the number of GRU layers as (Bangwayo-Skeete & Skeete, 2015;Kato & Yamamoto, 2020;Liu, Ullah, et al, 2020;Sakamanee et al, 2020;Wang, 2020), the input time as (Bian et al, 2020;Fu et al, 2016;Lin et al, 2020;Luan et al, 2022;Wu et al, 2004;Zhang et al, 2018), the number of hidden units as [16,32,64,100,128], and analyze the change of the prediction accuracy. show that when the learning rate value is 0.01, the RMSE is the smallest, and the accuracy is the highest.…”

Section: Experimental Results With Varying Parameter Settingsmentioning

confidence: 99%

“…First, the direct accessibility between scenic spots affects the traffic volumes at and near these scenic spots. For example, traffic congestion in one place propagates through the road network to other places (Luan et al, 2022). Therefore, the traffic jam near one scenic spot may cause a delay in traffic in another if the two spots are easily accessible from one to the other (e.g., directly connected by subway or bus).…”

Section: Introductionmentioning

confidence: 99%

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Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

Gao

Chiang

Zhang

et al. 2022

Transactions in GIS

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Traffic prediction for scenic spots is an important topic in modeling an urban traffic system. Existing traffic prediction approaches typically use raw traffic data and road networks without considering the physical environment and human–environment interaction. This article presents a novel traffic prediction model that considers: (1) the topological structure of the city road network; (2) the popularity and accessibility of each scenic spot in the city; and (3) the traffic volumes of nearby scenic spots. The proposed model first learns a series of traffic dependency graphs by the Multi‐graph Convolutional Network using multiple data sources describing historical traffic volumes, scenic spots popularity, land function, location, and accessibility. The graph nodes represent the scenic spots, and the links between them represent their traffic dependency, considering all traffic and geographic features. Then the proposed model uses the Gated Recurrent Unit (GRU) to capture the temporal dependency between multiple fused graphs for traffic volume prediction. The experiments show that the proposed model (M‐GCNGRU) can effectively exploit and integrate geographic data with historical traffic data for traffic volume prediction, outperforming several classical and state‐of‐the‐art methods.

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Section: Experimental Results With Varying Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

Gao

Chiang

Zhang

et al. 2022

Transactions in GIS

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“…Besides, BN [11,41] has also been applied to congestion propagation [9,24,45]. Other propagation models include the Gaussian mixture model [38], congestion tree structure [49], and Bayesian GCN [25]. Yet we focus on the root causes analysis instead of congestion propagation.…”

Section: Congestion Causes Analysismentioning

confidence: 99%

“…Causal analysis of congestion has been highly demanded in applications of intelligent transportation systems. There is emerging research applying classical DAGs for modeling the probabilistic dependency structure of congestion causes and analyzing the probability of traffic congestion given various traffic condition scenarios [1,17,25]. When mining the causality for traffic congestion, as the classical DAG-based solution, a traffic intersection is usually regarded as a DAG, whereas different congestion-related traffic variables (e.g., lane speed and signal cycle length) are treated as nodes.…”

Section: Introductionmentioning

confidence: 99%

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

Lan

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. This paper takes the traffic congestion analysis as a concrete case, where a traffic intersection is usually regarded as a DAG. In a road network of multiple intersections, different intersections can only have some overlapping and distinct variables observed. For example, a signalized intersection has traffic light-related variables, whereas unsignalized ones do not. This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized. To this end, we innovatively propose a multi-modal regression for linear causal relationship description of different variables. Then we develop a novel Causality Difference (𝐶𝐷) measure and its differentiable approximator. Compared with existing SOTA measures, 𝐶𝐷 can penalize the causal structural difference among DAGs with distinct nodes and can better consider the uncertainty of causal orders. We rigidly prove our design's topological interpretation and consistency properties. We conduct thorough simulations and one case study to show the effectiveness of our MM-DAG. The code is available under https://github.com/Lantian72/MM-DAG.

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“…Casual discovery aims to analyze causal relationships behind statistical correlations of different variables and facilitate better machine learning. Typical approaches to incorporate causal discovery include encoding features from domain-specific causal models as input to downstream tasks [18] and learning the structure of causal relationships between features for graph-based models [19,20]. As a powerful graph-based tool for modeling directed causal relationships between variables, Bayesian network (BN) is being applied in traffic prediction [19].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Heterogeneous Graph Convolution Network For Traffic Flow Prediction

Jin

et al. 2022

The Computer Journal

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Traffic prediction has attracted a lot of attention in recent years. However, it is challenging due to the dynamic and heterogeneous spatial–temporal correlations. Most of the existing methods adopt the attention mechanism to capture the dynamic spatial features, but it is difficult for the attention mechanism to learn the real-time change of spatial dependencies, which restrict accurate spatial dependencies learning. Furthermore, most methods are out at elbows when solving heterogeneous spatial–temporal data. To overcome these problems, we propose a novel traffic prediction model called Dynamic spatial–temporal Heterogeneous Graph Convolution Network. Different from the existing methods, we have designed a dynamic localized graph and a corresponding adaptive localized graph convolution network, which are capable of simultaneously capturing dynamic and heterogeneous spatial correlations. We propose a gated adaptive temporal convolution network to capture the temporal heterogeneity of traffic data and enjoy global receptive fields. Finally, a global correlations fusion network is provided to incorporate the global spatial–temporal correlations. Compared with 11 baselines, our proposed model achieves state-of-the-art performance in the accuracy of prediction.

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Traffic congestion propagation inference using dynamic Bayesian graph convolution network

Cited by 45 publications

References 24 publications

Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

A Dynamic Heterogeneous Graph Convolution Network For Traffic Flow Prediction

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