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Open Access

Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing

Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
State Key Laboratory of Cryptology, Beijing 100878, China
Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen 518000, China.
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Abstract

In recent years, the urbanization process has brought modernity while also causing key issues, such as traffic congestion and parking conflicts. Therefore, cities need a more intelligent "brain" to form more intelligent and efficient transportation systems. At present, as a type of machine learning, the traditional clustering algorithm still has limitations. K-means algorithm is widely used to solve traffic clustering problems, but it has limitations, such as sensitivity to initial points and poor robustness. Therefore, based on the hybrid architecture of Quantum Annealing (QA) and brain-inspired cognitive computing, this study proposes QA and Brain-Inspired Clustering Algorithm (QABICA) to solve the problem of urban taxi-stand locations. Based on the traffic trajectory data of Xi’an and Chengdu provided by Didi Chuxing, the clustering results of our algorithm and K-means algorithm are compared. We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means, and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%, up to approximately 83%, with higher robustness. QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum, and brain-inspired cognitive computing provides search feedback and direction. Thus, we will further consider applying our algorithm to analyze urban traffic flow, and solve traffic congestion and other key problems in intelligent transportation.

Keywords

cluster analysisintelligent transportationquantum annealing and brain-inspired clustering algorithmK-means

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Tsinghua Science and Technology
Volume 25 Issue 6,
December 2020
Pages 813-825
DOI: 10.26599/TST.2020.9010007
Cite this article:
Wang N, Guo G, Wang B, et al. Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing. Tsinghua Science and Technology, 2020, 25(6): 813-825. https://doi.org/10.26599/TST.2020.9010007

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Received: 03 March 2020
Accepted: 10 March 2020
Published: 07 May 2020
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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