Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Tang, Keshuang; Cao, Yumin; Chen, Can; Yao, Jiarong; Tan, Chaopeng; Sun, Jian

doi:10.1111/mice.12559

Cited by 31 publications

(15 citation statements)

References 47 publications

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“…The GCN was first proposed by LeCun et al. (1998) as inspired by the motivation of the CNN (Guo et al., 2020, 2021; Jeong et al., 2020; Tang et al., 2021; F. Wang et al., 2021). Graph neural network is a special neural network that can directly operate on graphic structural data.…”

Section: Gcn‐drl Modelmentioning

confidence: 99%

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A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions

Fan

Zhang

2022

Computer aided Civil Eng

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Water distribution networks (WDNs) are critical infrastructure for communities. The dramatic expansion of the WDNs associated with urbanization makes them more vulnerable to high-consequence hazards such as earthquakes, which requires strategies to ensure their resilience. The resilience of a WDN is related to its ability to recover its service after disastrous events. Sound decisions on the repair sequence play a crucial role to ensure a resilient WDN recovery. This paper introduces the development of a graph convolutional neural network-integrated deep reinforcement learning (GCN-DRL) model to support optimal repair decisions to improve WDN resilience after earthquakes. A WDN resilience evaluation framework is first developed, which integrates the dynamic evolution of WDN performance indicators during the post-earthquake recovery process. The WDN performance indicator considers the relative importance of the service nodes and the extent of post-earthquake water needs that are satisfied. In this GCN-DRL model framework, the GCN encodes the information of the WDN. The topology and performance of service nodes (i.e., the degree of water that needs satisfaction) are inputs to the GCN; the outputs of GCN are the reward values (Q-values) corresponding to each repair action, which are fed into the DRL process to select the optimal repair sequence from a large action space to achieve highest system resilience. The GCN-DRL model is demonstrated on a testbed WDN subjected to three earthquake damage scenarios. The performance of the repair decisions by the GCN-DRL model is compared with those by four conventional decision methods. The results show that the recovery sequence by the GCN-DRL model achieved the highest system resilience index values and the fastest recovery of system performance. Besides, by using transfer learning based on a pre-trained model, the GCN-DRL model achieved high computational efficiency in determining the optimal repair sequences under new damage scenarios. This novel GCN-DRL model features robustness and universality to support optimal repair decisions to ensure resilient WDN recovery from earthquake damages.

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Section: Gcn‐drl Modelmentioning

confidence: 99%

“…The GCN was first proposed by LeCun et al (1998) as inspired by the motivation of the CNN (Guo et al, 2020(Guo et al, , 2021Jeong et al, 2020;Tang et al, 2021;F. Wang et al, 2021).…”

Section: Gcnmentioning

confidence: 99%

A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions

Fan

Zhang

2022

Computer aided Civil Eng

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“…A considerable number of works have been conducted on traffic flow prediction [24,31,37], while Origin-Destination (OD) matrix prediction receives less attention for its greater complexity [27,35]. To date, related works on metro OD prediction are critically few [36,38].…”

Section: Related Work 51 Od Matrix Prediction In Traffic Domainmentioning

confidence: 99%

Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data

Ye¹,

Zhao²,

Furong³

et al. 2021

Preprint

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Accurate prediction of short-term OD Matrix (i.e. the distribution of passenger flows from various origins to destinations) is a crucial task in metro systems. It is highly challenging due to the constantly changing nature of many impacting factors and the real-time delayed data collection problem. Recently, some deep learning-based models have been proposed for OD Matrix forecasting in ridehailing and high way traffic scenarios. However, these models can not sufficiently capture the complex spatiotemporal correlation between stations in metro networks due to their different prior knowledge and contextual settings. In this paper we propose a hybrid framework Multi-view TRGRU to address OD metro matrix prediction. In particular, it uses three modules to model three flow change patterns: recent trend, daily trend, weekly trend. In each module, a multi-view representation based on embedding for each station is constructed and fed into a transformer based gated recurrent structure so as to capture the dynamic spatial dependency in OD flows of different stations by a global self-attention mechanism. Extensive experiments on three large-scale, real-world metro datasets demonstrate the superiority of our Multi-view TRGRU over other competitors.

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“…Other application contexts of DL—traffic flow prediction and traffic incident detection—were proposed initially by Adeli et al. (Ghosh‐Dastidar & Adeli, 2003; X. Jiang & Adeli, 2005) and subsequently investigated using advanced models such as graphic DL (Y. Zhang, Cheng, et al., 2019) and 3D CNN (Tang et al., 2020). Combined with another sibling (AI technique reinforcement learning (RL)) to yield DRL, DL has also been applied to operational control and planning tasks in transportation such as traffic signal control (Wu et al., 2019) and pavement maintenance planning (Yao et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Chen

Dong

et al. 2021

Computer aided Civil Eng

148

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A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

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Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Cited by 31 publications

References 47 publications

A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions

A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions

Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

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