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

Show Author's information Chao Wang1Wei Zhang1Sumin 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

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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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