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Cited by 140Traffic Prediction and Management TechniquesTraffic control and managementQuantum Computing Algorithms and Architecture

Temporal-Spatial Quantum Graph Convolutional Neural Network Based on Schrödinger Approach for Traffic Congestion Prediction

Qu, Zhiguo, Liu, Xinzhu, Zheng, Min·2023·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2022.3203791
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Abstract

Traffic congestion prediction (TCP) plays a vital role in intelligent transportation systems due to its importance of traffic management. Methods for TCP have emerged greatly with the development of machine learning. However, TCP is always a challenging work due to the dynamic characteristics of traffic data and the complex structure of traffic network. This paper presents a new quantum algorithm that can capture temporal and spatial features of traffic data simultaneously for TCP. The algorithm consists of the following steps. First, we give a closed-form solution in the Schrödinger approach theoretically to analyze this TCP problem in time dimension. Then we can get the temporal features from the solution. At last, we construct a quantum graph convolutional network and apply temporal features into it. Thus, the temporal-spatial quantum graph convolutional neural network is proposed. The feasibility of this method is proved through experiments on the simulation platform. The experimental results show the average error rate is 0.21 and can resist perturbation effectively.

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  • Although these models are highly optimised on the training data set, user acceptability remains under question.

    “Although these models are highly optimised on the training data set using [61,62], user acceptability remains under question.”

    From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [62]

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Authors
Qu, Z., Liu, X., & Zheng, M.
Affiliations
  • Beijing University of Posts and Telecommunications (CN)
  • Nanjing University of Information Science and Technology (CN)
  • Hubei University of Science and Technology (CN)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-08
Volume
24
Issue
8
Pages
8677-8686
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2022.3203791
OpenAlex
W4294982617
Cited by
140
References
6
Citations by year
2026: 16 · 2025: 32 · 2024: 60 · 2023: 32
FWCI
13.55
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Funders
  • National Natural Science Foundation of China (61373131, 62071240, 61601358)
  • Natural Science Foundation of Jiangsu Province (BK20171458)
  • Sichuan Youth Science and Technique Foundation (2017JQ0048)
  • Open Foundation of State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (SKLNST-2020-1-17)
  • PAPD and CICAEET Funds
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
Keywords
traffic congestion prediction, Schrödinger equation, spatio-temporal features, traffic networks, analytical solution, quantum machine learning, intelligent transportation systems

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