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Intelligent and Converged Networks 2022, 3(3): 260-270 https://doi.org/10.23919/ICN.2022.0017
Open Access | Issue | Published: 30 September 2022

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

Show Author's Information Chao Wang1,  Wei Zhang1,  Sumin Wang1,2( )
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
Department of Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China
Keywords:
intuitive reasoning, selective attention mechanism, quantum annealing algorithm, Quadratic Unconstrained Binary Optimization (QUBO) model, parking lot location
Cite this article:
Wang C, Zhang W, Wang S. An asymptotically optimal public parking lot location algorithm based on intuitive reasoning. Intelligent and Converged Networks, 2022, 3(3): 260-270. https://doi.org/10.23919/ICN.2022.0017
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Abstract Full text About this article

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand, this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem. Guided by the idea of intuitive reasoning, we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions. First, Selective Attention Mechanism (SAM) is introduced to reduce the search space by adaptively focusing on the important information in the features. Then, Quantum Annealing (QA) algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution. Experiments on the parking lot location dataset in Luohu District, Shenzhen, show that the proposed method has improved the accuracy and running speed of the solution, and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.


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About this article

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

Show Author's information Chao Wang1, Wei Zhang1, Sumin Wang1,2( )
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
Department of Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China

Abstract

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand, this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem. Guided by the idea of intuitive reasoning, we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions. First, Selective Attention Mechanism (SAM) is introduced to reduce the search space by adaptively focusing on the important information in the features. Then, Quantum Annealing (QA) algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution. Experiments on the parking lot location dataset in Luohu District, Shenzhen, show that the proposed method has improved the accuracy and running speed of the solution, and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.

Keywords: intuitive reasoning, selective attention mechanism, quantum annealing algorithm, Quadratic Unconstrained Binary Optimization (QUBO) model, parking lot location

References(28)

[1]
Z. Wang, Z. Tan, and H. Xu, Location model and algorithm of public parking facilities, in Proc. 2008 IEEE ICICTA, Changsha, China, 2008, pp. 598–602.
DOI
[2]

J. Shaw, F. Rudzicz, T. Jamieson, and A. Goldfarb, Artificial intelligence and the implementation challenge, J. Medical Internet Res., vol. 21, no. 7, p. e13659, 2019.
DOI Google Scholar
[3]

M. Ding, X. Lan, R. Peng, and N. Zheng, Progress and prospect of machine reasoning, (in Chinese), PR &AI, vol. 34, no. 1, pp. 1–13, 2021.
[4]

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al., Mastering the game of go without human knowledge, Nature, vol. 550, no. 7676, pp. 354–359, 2017.
DOI Google Scholar
[5]

N. Zheng, Z. Liu, P. Ren, N. Y. Ma, S. Chen, S. Yu, J. Xue, B. Chen, and F. Wang, Hybrid-augmented intelligence: Collaboration and cognition, Front. Inform. Technol. Electro. Eng., vol. 18, no. 2, pp. 153–179, 2017.
DOI Google Scholar
[6]

N. Lavie, A. Hirst, J. W. D. Fockert, and E. Viding, Load theory of selective attention and cognitive control, J. Exp. Psychol. Gen., vol. 133, no. 3, pp. 339–354, 2004.
DOI Google Scholar
[7]
I. L. Janis and L. Mann, Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment. New York, NY, USA: Free Press, 1977.
[8]

I. A. Hiskens and R. J. Davy, Exploring the power flow solution space boundary, IEEE Trans. Power Syst., vol. 16, no. 3, pp. 389–395, 2001.
DOI Google Scholar
[9]

S. Chen, Z. Jian, Y. Huang, Y. Chen, Z. Zhou, and N. Zheng, Autonomous driving: Cognitive construction and situation understanding, Sci. China Inf. Sci., vol. 62, no. 8, pp. 71–97, 2019.
DOI Google Scholar
[10]

R. Banerjee and B. R. Majhi, Quantum tunneling and back reaction, Phys. Lett. B, vol. 662, no. 1, pp. 62–65, 2008.
DOI Google Scholar
[11]

A. B. Finnila, M. A. Gomez, C. Sebenik, C. Stenson, and J. D. Doll, Quantum annealing: A new method for minimizing multidimensional functions, Chem. Phys. Lett., vol. 219, nos. 5&6, pp. 343–348, 1994.
DOI
[12]

S. Wang, Z. Pei, C. Wang, and J. Wu, Shaping the future of the application of quantum computing in intelligent transportation system, Intelligent and Converged Networks, vol. 2, no. 4, pp. 259–276, 2021.
DOI Google Scholar
[13]

F. Neukart, G. Compostella, C. Seidel, D. V. Dollen, S. Yarkoni, and B. Parney, Traffic flow optimization using a quantum annealer, Front. ICT, vol. 4, p. 29, 2017.
DOI Google Scholar
[14]

D. Inoue, A. Okada, T. Matsumori, K. Aihara, and H. Yoshida, Traffic signal optimization on a square lattice with quantum annealing, Sci. Rep., vol. 11, no. 1, p. 3303, 2021.
DOI Google Scholar
[15]

N. Wang, G. Guo, B. Wang, and C. Wang, 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, vol. 25, no. 6, pp. 813–825, 2020.
DOI Google Scholar
[16]
M. Streif, F. Neukart, and M. Leib, Solving quantum chemistry problems with a D-wave quantum annealer, in Proc. 2019 International Workshop on Quantum Technology and Optimization Problems, Munich, Germany, 2019, pp. 111–122.
DOI
[17]

A. Teplukhin, B. K. Kendrick, S. M. Mniszewski, Y. Zhang, A. Kumar, C. F. Negre, P. M. Anisimov, S. Tretiak, and P. A. Dub, Computing molecular excited states on a D-wave quantum annealer, Sci. Rep., vol. 11, no. 1, p. 18796, 2021.
DOI Google Scholar
[18]
B. Pokharel, D. Venturelli, and E. Rieffel, Performance of quantum annealers on hard scheduling problems, in Proc. APS March Meeting Abstracts, New Orleans, LA, USA, 2017, p. B51. 00007.
[19]

M. Eskandari and A. S. Nookabadi, Off-street parking facility location on urban transportation network considering multiple objectives: A case study of Isfahan (Iran), Transport, vol. 33, no. 4, pp. 1067–1078, 2018.
DOI Google Scholar
[20]

A. C. Aydinoglu and A. S. Iqbal, Determining parking demand and locating parking areas using geographic analytics methods, J. Urban Plan. Dev., vol. 147, no. 1, pp. 5020035.01–5020035.12, 2021.
DOI Google Scholar
[21]

T. Shen, K. Hua, and J. Liu, Optimized public parking location modelling for green intelligent transportation system using genetic algorithms, IEEE Access, vol. 7, pp. 176870–176883, 2019.
DOI Google Scholar
[22]

W. Liang, J. Hu, Y. Zhang, Z. Zhang, and Z. Wang, Modeling and empirical analyses of parking space choices based on individual preferences, (in Chinese), Journal of Tsinghua University (Science and Technology), vol. 57, no. 1, pp. 100–106, 2017.
Google Scholar
[23]

A. Das and B. K. Chakrabarti, Colloquium: Quantum annealing and analog quantum computation, Rev. Mod. Phys., vol. 80, no. 3, pp. 1061–1081, 2008.
DOI Google Scholar
[24]

T. D. Kieu, The travelling salesman problem and adiabatic quantum computation: An algorithm, Quantum Inf. Process., vol. 18, no. 3, p. 90, 2019.
DOI Google Scholar
[25]
Shenzhen Government Data Open Platform, https://opendata.sz.gov.cn/, 2021.
[26]

L. P. Wang, Y. Ren, Q. C. Qiu, and F. Y. Qiu, Survey on performance indicators for multi-objective evolutionary algorithms, Chin. J. Comput., vol. 44, no. 8, pp. 1590–1619, 2021.
Google Scholar
[27]
D. A. V. Veldhuizen and G. B. Lamont, Evolutionary computation and convergence to a Pareto front, in Late Breaking Papers at the Genetic Programming 1998 Conference, J. R. Koza, ed. Stanford, CA, USA: Stanford University Bookstore, 1998, pp. 221–228.
[28]
A. Menchaca-Mendez and C. A. C. Coello, GD-MOEA: A new multi-objective evolutionary algorithm based on the generational distance indicator, in Proc. 8th International Conference on Evolutionary Multi-Criterion Optimization, Guimarães, Portugal, 2015, pp. 156–170.
DOI
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Publication history

Received: 03 July 2022
Revised: 21 July 2022
Accepted: 28 August 2022
Published: 30 September 2022
Issue date: September 2022

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© All articles included in the journal are copyrighted to the ITU and TUP.

Acknowledgements

Acknowledgment

This study was supported by the Special Zone Project of National Defense Innovation and the Science and Technology Program of Education Department of Jiangxi Province (No. GJJ171503).

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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