Анализ данных сервиса платной парковки для создания эффективной системы ценообразования (на примере Владивостока)
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Analysis of paid parking service data to create an effective pricing system (using the example of Vladivostok)

idEryomin I.R. idNikitin P.V.

UDC 004.048
DOI: -

  • Abstract
  • List of references
  • About authors

The problem of allocation and operation of parking spaces is an important part of research in the field of intelligent transportation. In recent years, due to the sharp increase in the number of cars, the problem of limited parking space resources has been expressed. Effective parking management requires analysis of huge amounts of data and modeling to optimize the use of parking spaces. The implementation and operation of smart paid parking space in Vladivostok creates an interesting application area for data mining and machine learning. The study uses a large-scale data set of historical parking transactions in Vladivostok, including vehicle type, time, location, session duration, and more, to create a data model that reflects the relationship between parking prices, demand, and revenue. The article describes the mechanism for creating a data model that includes all important aspects of the functioning of paid parking lots and factors affecting occupancy. Using this model will allow for machine learning, application of models and evaluation of the effectiveness of their application. The study also identifies key factors influencing parking demand, such as time of day, day of week, location, etc. The data model and insights gained from this research can be used by governments and property owners to optimize the use of paid parking and improve traffic management in smart cities. The approach presented in this article can be applied to other cities to create data-driven pricing systems that meet the specific needs and characteristics of each city.

1. Parmar J., Das P., Dave S.M. Study on demand and characteristics of parking system in urban areas: A review. Journal of Traffic and Transportation Engineering (English Edition). 2020;7(1):111–124. https://doi.org/10.1016/j.jtte.2019.09.003

2. Lautso K. Mathematical relationships among parking characteristics and revising and reduction methods of parking field survey information. Transportation Research Part B: Methodological. 1981;15(2):73–83. https://doi.org/10.1016/0191-2615(81)90034-5

3. Allocation of parking demand in a CBD. URL: https://trid.trb.org/View/116002 (Accessed 10th January 2024).

4. Kotb A.O., Shen Y.-C., Zhu X., Huang Y. iParker – A New Smart Car-Parking System Based on Dynamic Resource Allocation and Pricing. IEEE Transactions on Intelligent Transportation Systems. 2016;17(9):2637–2647. https://doi.org/10.1109/TITS.2016.2531636

5. Zhang X., Pitera K., Wang Y. Parking reservation techniques: A review of research topics, considerations, and optimization methods. Journal of Traffic and Transportation Engineering (English Edition). 2023;10(6):1099–1117. https://doi.org/10.1016/j.jtte.2023.07.009

6. Wang H., He W. A Reservation-based Smart Parking System. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 10-15 April 2011, Shanghai, China. IEEE; 2011. P. 690–695. https://doi.org/10.1109/INFCOMW.2011.5928901

7. Lu X.-S., Huang H.-J., Guo R.-Y., Xiong F. Linear location-dependent parking fees and integrated daily commuting patterns with late arrival and early departure in a linear city. Transportation Research Part B: Methodological. 2021;150:293–322. https://doi.org/10.1016/j.trb.2021.06.012

8. Koryagin M.E., Vylegzhanin I.A. Nash equilibrium in allocating space for paid and free parking, taking into account the interests of motorists, city authorities, and parking owners. T-Comm – Telekommunikatsii i Transport = T-Comm. 2022;16(7):36–43. (In Russ.). https://doi.org/10.36724/2072-8735-2022-16-7-36-43

9. Tasseron G., Martens K. The Impact of Bottom-Up Parking Information Provision in a Real-Life Context: The Case of Antwerp. Journal of Advanced Transportation. 2017;2017(1). https://doi.org/10.1155/2017/1812045

10. Morozov A.S., Kontsevik G.I., Shmeleva I.A., Schneider L., Zakharenko N., Budenny S., Mityagin S.A. Assessing the transport connectivity of urban territories, based on intermodal transport accessibility. Frontiers in Built Environment. 2023;9. https://doi.org/10.3389/fbuil.2023.1148708

11. Shoup D. Parking and the City. New York, NY: Routledge; 2018. 534 p.

12. Zoeter O., Dance C., Clinchant S., Andreoli J.-M. New Algorithms for Parking Demand Management and a City-Scale Deployment. In: KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: Proceedings, 24 27 August 2014, New York, USA. New York: Association for Computing Machinery; 2014. P. 1819–1828. https://doi.org/10.1145/2623330.2623359

Eryomin Ivan Romanovich

WoS | ORCID | eLibrary |

Financial University under the Government of the Russian Federation

Moscow, Russian Federation

Nikitin Petr Vladimirovich
Candidate of Pedagogy, Associate Professor

ORCID | eLibrary |

Financial University under the Government of the Russian Federation

Moscow, Russian Federation

Keywords: modeling, paid parking lots, data analysis, gaussian distribution, optimization

For citation: Eryomin I.R. Nikitin P.V. Analysis of paid parking service data to create an effective pricing system (using the example of Vladivostok). Modeling, Optimization and Information Technology. 2024;12(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1585 DOI: - (In Russ).

47

Full text in PDF

Received 23.05.2024

Revised 30.05.2024

Accepted 10.06.2024