Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 34, 10 May 2024


Open Access | Article

Parking recommendation with meta-heuristic algorithms

Siyuan Wu 1 , Tingting Yang * 2
1 Pomfret School
2 Technology Innovation Center at TAIKANG LIFE INSURANCE Co., Ltd.

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 34, 274-283
Published 10 May 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Siyuan Wu, Tingting Yang. Parking recommendation with meta-heuristic algorithms. TNS (2024) Vol. 34: 274-283. DOI: 10.54254/2753-8818/34/20241147.

Abstract

Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list of potential parking locations based on the user’s travel destinations. The primary objectives of these algorithms include minimizing travel distance, reducing total parking fees, and selecting parking lots with ample available spaces. The proposed model incorporates bio-inspired algorithms, including simulated annealing, genetic algorithms, and their adaptive variants. Our evaluation compares the performance of these algorithms, highlighting the adaptive simulated annealing’s superior quality of solutions and robustness against local minima. However, it is important to note that this approach comes with a trade-off, requiring longer execution times. In summary, this research contributes a novel parking lot recommendation model that effectively addresses the challenges posed by urban parking. The performance evaluation underscores the efficacy of the adaptive simulated annealing approach, showcasing its potential for practical implementation despite its relatively longer execution time.

Keywords

heuristic algorithms, parking lot model, simulated annealing, genetic algorithms

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
ISBN (Print)
978-1-83558-369-2
ISBN (Online)
978-1-83558-370-8
Published Date
10 May 2024
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/34/20241147
Copyright
10 May 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated