A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks

Author:

Zhu Difeng12,Shen Guojiang1ORCID,Chen Jingjing3ORCID,Zhou Wenfeng1,Kong Xiangjie1ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

2. Yuanpei College, Shaoxing University, Shaoxing 312000, China

3. School of Economics, Fudan University, Shanghai 200433, China

Abstract

Due to the incomplete coverage and failure of traffic data collectors during the collection, traffic data usually suffers from information missing. Achieving accurate imputation is critical to the operation of transportation networks. Existing approaches usually focus on the characteristic analysis of temporal variation and adjacent spatial representation, and the consideration of higher-order spatial correlations and continuous data missing attracts more attentions from the academia and industry. In this paper, by leveraging motif-based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing. First, through motif discovery, the higher-order graph aggregation model was presented in traffic networks. It utilized graph convolution network (GCN) to polymerize the correlated segment attributes of the missing data segments. Then, the multitime dimension imputation model based on bidirectional long short-term memory (Bi-LSTM) incorporated the recent, daily-periodic, and weekly-periodic dependencies of the historical data. Finally, the spatial aggregated values and the temporal fusion values were integrated to obtain the results. We conducted comprehensive experiments based on the real-world dataset and discussed the case of random and continuous data missing by different time intervals, and the results showed that the proposed approach was feasible and accurate.

Funder

Fundamental Research Funds for the Provincial Universities of Zhejiang

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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3. SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features;2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS);2023-06-14

4. Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network;Neurocomputing;2023-04

5. Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data;Knowledge-Based Systems;2023-02

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