DOI: 10.5937/jaes18-23911
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
Volume 18 article 656 pages: 26 - 39
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.
The authors express their gratitude to the Department for
Transport and Road Infrastructure Development of the
Moscow City Government for the grant and the data provided
for the study, as well as the faculty members and
students of Plekhanov Russian University of Economics
who participated in this study.
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