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Российский экономическийУНИВЕРСИТЕТ
имени Г.В. Плеханова

Основан в 1907 году

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Modeling of the need for parking space in the districts of Moscow metropolis by using multivariate methods

Наименование публикации:Modeling of the need for parking space in the districts of Moscow metropolis by using multivariate methodsАвторы:Скоробогатых И. И. , Сидорчук Р. Р., Лукина А. В., Мхитарян С. В., Стукалова А. А.
Тематическая область:Экономика и экономические науки
Вид публикации:Статья в журнале
Электронная публикация:НетЯзык издания:АнглийскийГод издания:2020Страна издания: Словения Наименование журнала или сборника:Journal of Applied Engineering ScienceНомер журнала (с указанием года):Volume 18, Issue 1, 2020, Pages 26-39Наименование издательства:Institut za Istrazivanja I Projektovanja u PrivrediКод ISSN или ISBN:1451-4117Количество страниц:13Количество печатных листов:0,9Тираж, экз:1000Индексация:РИНЦ,
Scopus,
ERIH,
Google Scholar,
Web of Science,
Repec
Библиографическая ссылка:Sidorchuk R.R., Skorobogatykh I.I., Mkhitaryan S.V., Lekina A.V., Stukalova A.A. (2020)Modeling of the need for parking space in the districts of Moscow metropolis by using multivariate methods. Journal of Applied Engineering Science/ Volume 18, Issue 1, 2020, Pages 26-39Аннотация (реферат):

The growth of metropolis cities and consequently the number of vehicles cruising within their boundaries create a permanent problem of dissatisfaction with the amount of parking space and its over-occupancy. The results of continuous observation of parking lots in Moscow and data on registered cars in the city districts was the initial basis for this study. The data was processed by IBM SPSS Statistics 20 statistical program to obtain descriptive statistics indicators of parking space in Moscow, the analysis of cause-and-effect relations and subsequent multivariate modeling using regression analysis; log it regression; discriminant analysis; “classification trees” (decision tree). The results clearly show the possibility of applying the methods of multivariate statistics, log it regression and “classification trees”. Both models allow for using the explanatory variables “proportion of parking lots with violations” and “number of parking spaces in the street and road network” to analyze the impact on parking lot occupancy. Also, the descriptive statistics analysis revealed that when the number and proportion of parking lots with violations are 2 times higher on average in the districts with over-occupied parking lots versus the districts where the parking lot occupancy is not so high, and the number of paid parking lots is over 10 times less. The increase in the proportion of parking spaces with violations ranging from 0 to 0.2% entails a sharp increase in parking space occupancy (up to 90%), while a further increase in the proportion of parking spaces with violations does not entail a significant increase in the parking occupancy. 


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