SMART PARKING MANAGEMENT ALGORITHMS IN SMART CITIES
DOI:
https://doi.org/10.5281/zenodo.20048301Keywords:
Smart cities, parking spaces, parking management, Genetic Algorithm, equitable distribution, required parameters, proposed algorithmAbstract
Recently, various advanced technologies have been employed to build smart cities. Smart cities
aim to improve the quality of life by delivering better services. One of the essential services for any smart city
is the availability of sufficient parking spaces to ensure smooth and efficient traffic flow.
This research proposes a new framework for solving the problem of parking lot allocation, emphasizing the
equitable distribution of users based on the total number of people in each parking area. The allocation process
is carried out while considering the available parking lots in each area.
To achieve this goal, the study develops a set of seven algorithms aimed at reducing the gap in the number
of people between parking areas. Numerous experiments were conducted on 2,430 different cases to evaluate
aspects such as execution time and gap calculations, in order to assess the performance of the developed
algorithms.
The analysis of the obtained results indicates strong performance of the proposed algorithms. It also
demonstrates that the algorithms effectively solve the studied problem in terms of both gap reduction and
computational time. The MR algorithm achieved excellent performance compared to one of the best algorithms
in the literature, with an accuracy of 96.1%, an average gap of 0.02, and an execution time of 0.007 s.
References
Chen, C., Yuan, Z., & Zhu, H. (2020). Playing, parenting, and family leisure in parks: Exploring emotional geographies
of families in Guangzhou Children’s Park, China. Children’s Geographies, 18, 463–476. https://doi.org/10.1080/1473
2019.1676879
Reuben, A., Rutherford, G. W., James, J., & Razani, N. (2020). Association of neighborhood parks with child health in
the United States. Preventive Medicine. https://doi.org/10.1016/j.ypmed.2020.106265
Hanifi, M., & Heidarzadeh, K. (2020). Developing a scale to measure “comfortable shopping experience” in shopping
malls. Journal of Business Management, 12, 227–242. https://doi.org/10.22059/JIBM.2018.261571.3130
Jaeger, E. C., & Smith, A. C. (2020). Introduction to the Natural History of Southern California. University of California
Press.
Burris, A. (2021). Measuring intrinsic traits of children at zoos. Interdisciplinary Journal of Environmental Science
Education, 17, e2246. https://doi.org/10.21601/ijese/10939
Lejdel, B. A. (2020). A conceptual framework for modeling smart parking. In Application of Expert Systems: Theoretical
and Practical Aspects. IntechOpen. https://doi.org/10.5772/intechopen.85202
Winter, K., Cats, O., Martens, K., & van Arem, B. (2021). Parking space for shared automated vehicles: How less can
be more. Transportation Research Part A: Policy and Practice, 143, 61–77. https://doi.org/10.1016/j.tra.2020.11.008
Суяров, А. Б. (2025-01). Технологии организации дистанционного обучения. Phonex Publikation, II-son.
Geng, Y., & Cassandras, C. G. (2012). A new “smart parking” system infrastructure and implementation. Procedia -
Social and Behavioral Sciences, 54, 1278–1287. https://doi.org/10.1016/j.sbspro.2012.09.842
Mladenović, M., Delot, T., Laporte, G., & Wilbaut, C. (2020). The parking allocation problem for connected vehicles.
Journal of Heuristics, 26, 377–399. https://doi.org/10.1007/s10732-017-9364-7
Karimov, A. (2023). Parameters justification of the improved potato digger. Innovative Development in Educational
Activities, 2(18), 256–263.
Karimov, A. A. (2024-06). Organizing management in the transport logistics system. American Journal of Engineering,
Mechanics and Architecture, 2(6), 66–68.
