MODELING OF THE NEED FOR PARKING SPACE IN THE DISTRICTS OF MOSCOW METROPOLIS BY USING MULTIVARIATE METHODS
Abstract
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 objectives of this study were: to obtain descriptive statistics of the indicators of parking space available in Moscow; to identify the factors of influence on the basis of the analysis of cause-and-effect relations between the explanatory indicators of parking space and parking space occupancy; to analyze and verify the effectiveness of predictive models for determining the parking occupancy. An observation method was used to obtain the data. 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; logit regression; discriminant analysis; “classification trees” (decision tree). For the subsequent processing, the received data on observations of parking space of Moscow was assumed in this paper including: a list of parking spaces with indication of city district for each parking lot, parking ring reference, number of parking spaces, number of parked cars, number of cars parked with violations, position relative to the street and road network (roadside, SRN), parking tariff, method of placing (parking or storage). Parking occupancy exceeds 90% in 50 districts. The average value is 14.46 thousand parking spaces and 3.60 thousand storage spaces. The results clearly show the possibility of applying the methods of multivariate statistics, logit 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.
References
Administrative Territories and Districts of Moscow http://mosopen.ru/regions, accessed on 2019-08-19.
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.
Battaïa, G., Gardrat, M., Toilier, F., Le Van, E., Zuccarello, Ph. (2016). Simulating Logistic Innovation in a Growing Urban Environment Transportation Research Procedia, vol. 12, 489-499.
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.
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.
Dubrov A.M., Mhitarjan V.S., Troshin L.I. (2011) Multivariate Statistic Methods. Finance and Statistics, moscow.
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.
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.
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.
Franco, S.F. (2017, July). Downtown parking supply, work-trip mode choice and urban spatial structure. Transportation Research Part B: Methodological, vol. 101, 107-122.
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.
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.
Khinchin, A.Ya. (1963). Studies on Math Queuing Theory (Vol. 236). Fizmatgiz, Moscow.
Kleinrock, L. (1975). Queuing Systems, Volume I: Theory.
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.
Kumar, V. T. M. (2018). Smart Metropolitan Regional Development: Economic and Spatial Design Strategies. Smart Metropolitan Regional Development, 3-97.
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.
Levy, N., Render, M., Benenson, I., (2015, April). Spatially explicit modeling of parking search as a tool for urban parking facilities and policy assessment Transport Policy, vol. 39, 9-20.
Ma, K., Kang, E. (2011, July). Time–space convergence and urban decentralization. Journal of Transport Geography, vol. 19, Issue 4, 606-614.
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.
Malhotra, N.K., Birks, D. F. (2007). Marketing Research: An Applied Orientation. Pearson Education, New York.
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.
Moscow Transport Rings. The History. https://moscowchronology.ru/transport_rings.html, accessed on 2019-09-19.
Moscow Transport System Development. http://transport.mos.ru/common/upload/public/Инфоцентр/транспортная%20система%20Москвы_rus.pdf, accessed on 2019-03-10.
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.
Muramatsu, T., Oguchi, T. (2016). Proposal and Application of Parking Area Performance Measurement Methodology. Transportation Research Procedia, vol. 15, 628-639.
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).
Paula, M. (2006). Inimigo Invisivel: Metais Pesados E A Saude Humana. Periódico Tchê Química, vol. 3, no. 6, 37-44.
Pel, A.J., Chaniotakis, E. (2017). Stochastic User Equilibrium Traffic Assignment with Equilibrated Parking Search Routes. Transportation Research Part B: Methodological, vol. 101, 123-139.
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.
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 Artificial Intelligence, p. 278-282.
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.
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.
Sobolev, A.N. (2008) The Classification and Identification 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.
Sweet, M.N., Ferguson, M.R. (2019, June). Parking demand management in a relatively uncongested university setting Case Studies on Transport Policy, Volume 7, iss. 2, 453-462.
Thomas, D., Kovoor, B.C. (2018). A Genetic Algorithm Approach to Autonomous Smart Vehicle Parking System. Procedia Computer Science, vol. 125, 68-76.
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).
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.
Zhang, J., Zheng, W. (2016). Research on Stochastic Behavior of Traffic flow. Journal of Southwest Jiaotong University, vol. 51, no. 3.
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.
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.
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.
