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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.
1. Administrative Territories and Districts of Moscow http://mosopen.ru/regions, accessed on 2019-08-19.
2. Almeida, J.C., Moreira, A., Moreira, L., Arbilla, G. (2008). Primary Emission Ratios Obtained from the Monitoring of Criteria Pollutants in Rebouças Tunnel, Rio De Janeiro, Brazil. Periódico Tchê Química, vol. 5, no. 9, 13-18.
3. Dominici, A., De Gentili, E., Capocchi, L., & Santucci, J. F. (2018). Smart Parking: Integration and Data Management by Modeling and Simulation Using Connected Objects According to the DEVS Formalism. Proceedings of the 2018 4th International Conference on Universal Village (UV), p.1-4.
4. DTRID Contract No. 190. (2018) The Study of the Parking Spaces for Moscow Citizens and Recommendations for Their Effective Use. Department for Transport and Road Infrastructure Development of the Moscow City Government (DTRID). Research project, Reg. No. Research, Development and Technological Work (RDTW) AAAAAH-A19-119062190046-5.
5. Dubrov A.M., Mhitarjan V.S., Troshin L.I. (2011) Multivariate Statistic Methods. Finance and Statistics, Moscow.
6. Fedorenko, R.V., Zaychikova, N.A., Abramov, D.V., Vlasova, O.I. (2016). Nash equilibrium design in the interaction model of entities in the customs service system. Mathematics Education, vol. 11, no. 7, p. 2732-2744.
7. Fickling D., He, E. (2018) The Future of Transport is the Future of Cities. Bloomberg. https:// www.bloomberg.com/opinion/articles/2018-11-06/ polluted-megacities-push-transport-s-future-toward- the-rails, accessed on 2019-03-10.
8. Fithra, H., Sirojuzilam, H., Saleh, S. M., Saputra, J. (2019). Road Network Connectivity and Freight Transportation for Supporting the Development of the Northern Zone of Aceh. Journal of Southwest Jiaotong University, vol. 54, no. 3.
9. Gong, Q., Midlam-Mohler, S., Serra, E., Marano, V., & Rizzoni, G. (2013). PEV Charging Control for a Parking Lot Based on Queuing Theory. 2013 American Control Conference, p. 1124-1129.
10. Ionita, A., Pomp, A., Cochez, M., Meisen, T., & Decker, S. (2018). Where to Park?: Predicting Free Parking Spots in Unmonitored City Areas. Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, p. 22.
11. Khinchin, A.Ya. (1963). Studies on Math Queuing Theory (Vol. 236). Fizmatgiz, Moscow.
12. Kleinrock, L. (1975). Queuing Systems, Volume I: Theory.
13. Krpan, L., Maršanić, R., Milković, M. (2017). A Model of the Dimensioning of the Number of Service Places at Parking Lot Entrances by Using the Queuing Theory. Tehnički vjesnik, vol. 24, no. 1, 231-238.
14. Latif, M., Singla, S., Vadav, V. (2019). Modeling Off- Street Parking Based on User’s Behavior Using SPSS Software, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8, 2360-2372.
15. Ma, J., Clausing, E., & Liu, Y. (2017). Smart On- Street Parking System to Predict Parking Occupancy and Provide a Routing Strategy Using Cloud-Based Analytics. SAE Technical Paper, no. 2017-01-0087.
16. Malhotra, N.K., Birks, D. F. (2007). Marketing Research: An Applied Orientation. Pearson Education, New York.
17. Mingardo, G., van Wee, B., Rye, T. (2015). Urban Parking Policy in Europe: A Conceptualization of Past and Possible Future Trends. Transportation Research Part A: Policy and Practice, vol. 74, 268- 281.
18. Moscow Transport Rings. The History. https://moscowchronology. ru/transport_rings.html, accessed on 2019-09-19.
19. Moscow Transport System Development. http://transport. mos.ru/common/upload/public/Инфоцентр/ транспортная%20система%20Москвы_rus.pdf, accessed on 2019-03-10.
20. Mouchili, M.N., Aljawarneh, S., Tchouati, W. (2018). Smart City Data Analysis. Proceedings of the First International Conference on Data Science, E-learning and Information Systems, p. 33.
21. Muramatsu, T., Oguchi, T. (2016). Proposal and Application of Parking Area Performance Measurement Methodology. Transportation Research Procedia, vol. 15, 628-639.
22. Pandey, D., Hanchate, S. (2018) Navigation-Based Intelligent Parking Management System Using Queuing theory and IOT. 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT).
23. Paula, M. (2006). Inimigo Invisivel: Metais Pesados E A Saude Humana. Periódico Tchê Química, vol. 3, no. 6, 37-44.
24. Pel, A.J., Chaniotakis, E. (2017). Stochastic User Equilibrium Traffi c Assignment with Equilibrated Parking Search Routes. Transportation Research Part B: Methodological, vol. 101, 123-139.
25. Progressive Tariff for Parking Will Help Avoid Parking Lot Over-Occupancy in the Capital Downtown (2015). https://marino.mos.ru/presscenter/news/detail/ 2025650.html, accessed on 2019-08-19.
26. Richter, F., Di Martino, S., Mattfeld, D.C. (2014). Temporal and Spatial Clustering for a Parking Prediction Service. Proceedings of the 2014 IEEE 26th International Conference on Tools with Artifi cial Intelligence, p. 278-282.
27. Sidorchuk, R.R., Murtuzalieva, T.V., Skorobogatykh I.I., Musatov, B.I. (2018). Marketing Management System of Continuous Control for Indicators of Expectations of Consumers and Use of Big Data. Annals of Marketing MBA, vol. 2.
28. Sidorchuk, R., Musatova, Z., Mkhitaryan, S., Nevostruev, P., Komleva, N. (2016). Smart Technologies in Public Transport and Their Perception by the Youth Audience. Indian Journal of Science and Technology, vol. 9, no. 42, 10-17485.
29. Sobolev, A.N. (2008) The Classifi cation and Identifi - cation of Economic Models and Types of Transportations with Associated Transport Services: Author's Abstract ... by the Candidate of Economic Science: 08.00.05 Far Eastern State Transport University]. Khabarovsk.
30. Thomas, D., Kovoor, B.C. (2018). A Genetic Algorithm Approach to Autonomous Smart Vehicle Parking System. Procedia Computer Science, vol. 125, 68-76.
31. Thong, P.H., Soh, A.C., Jaafar, H., Ishak, A.J. (2013). Real-Time Monitoring System for Parking Space Management Services. Proceedings of the 2013 IEEE Conference on Systems, Process & Control (ICSPC).
32. Yang, Z., Gao, Ch., Chen, D. (2016). Optimization of Parking Supply in Central Business District Based on the Relationship Between Car Travelling and Car Parking. Systems Engineering Theory and Practice, vol. 8, no. 19, 2091-2010.
33. Zhang, J., Zheng, W. (2016). Research on Stochastic Behavior of Traffi c fl ow. Journal of Southwest Jiaotong University, vol. 51, no. 3.
34. Zhang, M.J., Zhang, Z.B., Du, X.L. (2017). Research on Number of Car Elevators for Cylindrical Underground Garage Based on Queuing Theory. China J. Highw. Transp, vol. 30, pp. 133-139.
35. Zheng, Y., Rajasegarar, S., Leckie, C., Palaniswami, M. (2014, April). Smart Car Parking: Temporal Clustering and Anomaly Detection in Urban Car Parking. Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), p. 1-6.
36. Zhenyu, M. E. I., Xiang, Y., Jun, Chen, Jun, & Wei, W. (2010). Optimizing Model of Curb Parking Pricing Based on Parking Choice Behavior. Journal of Transportation Systems Engineering and Information Technology, vol. 10, no. 1, 99-104.