The aim of the system with reservations is to reduce the time the user needs to reach the parking space as well as to rationalize the controlling of demands in the central business districts. When applying the system with reservations, it is necessary to know the user’s travel time to the parking space, as well as the time of parking. The sum of these two periods represents the parking space “occupancy”. The purpose of this paper is to suggest a model for determining the total occupancy of a parking space based on 1) the user’s travel time to the parking space; 2) the user’s duration of parking. Considering the fact that we are dealing with values which cannot be exactly estimated, the fuzzy logic system (FLS) is used. A Neural Network (NN) is trained on the basis of data about the estimated values of the input parameters and the real value of output parameters. Thus, a hybrid model of fuzzy logic and neural networks (ANFIS) is obtained. Finally, there is an example based on the real data which shows the application possibilities of this model.
Polycarpou E, Lambrinos L, Protopapadakis E. Smart parking solutions for urban areas. Proceedings of the 14th International Symposium on World of Wireless, Mobile and Multimedia Networks (WoWMoM), 4-7 June 2013, Madrid, Spain. IEEE; 2013. p. 1-6. Available from: https://www.computer.org/csdl/proceedings/wowmom/2013/5827/00/06583499-abs.html [Accessed 16th Oct. 2018].
Doulamis N, Protopapadakis E, Lambrinos L. Improving service quality for parking lot users using intelligent parking reservation policies. Proceedings of the 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 25-28 March 2013, Barcelona, Spain. IEEE; 2013. p. 1392-1397. Available from: https://ieeexplore.ieee.org/abstract/document/6550590 [Accessed 16th Oct. 2018].
Kaspi M, Raviv T, Tzur M. Parking reservation policies in one-way vehicle sharing systems. Transportation Research Part B: Methodological. 2014;62: 35-50. Available from: https://www.sciencedirect.com/science/article/pii/S0191261514000162 [Accessed 16th Oct. 2018].
Venkateswaran V, Prakash N. Intelligent approach for smart car parking reservation and security maintenance system. IJRET: International Journal of Research in Engineering and Technology. 2014;3(2): 248-251.
Shiyao C, Ming W, Chen L, Na R. The research and implement of the intelligent parking reservation management system based on zigbee technology. Proceedings of the 6th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 10-11 Jan. 2014, Zhangjiajie, China. IEEE; 2014. p. 741-744. Available from: https://ieeexplore.ieee.org/abstract/document/6802800 [Accessed 16th Oct. 2018].
Chen Z, Yin Y, He F, Lin JL. Parking reservation for managing downtown curbside parking. Transportation Research Record: Journal of the Transportation Research Board. 2015;2498: 12-18. Available from: https://trrjournalonline.trb.org/doi/abs/10.3141/2498-02 [Accessed 16th Oct. 2018].
Sheelarani P, Anand SP, Shamili S, Sruthi K. Effective car parking reservation system based on internet of things technologies. Proceedings of the World Conference onFuturistic Trends in Research and Innovation for Social Welfare (Startup Conclave), 29 Feb - 1 Mar 2016, Coimbatore, India. IEEE; 2016. p. 1-4. Available from: https://ieeexplore.ieee.org/abstract/document/7583962 [Accessed 16th Oct. 2018].
Boudali I, Ouada MB. Smart parking reservation system based on distributed multicriteria approach. Applied Artificial Intelligence. 2017;31(5-6): 518-537. Available from: https://www.tandfonline.com/doi/abs/10.1080/08839514.2017.1378275 [Accessed 16th Oct. 2018].
Tasseron G, Martens K. Urban parking space reservation through bottom-up information provision: An agent-based analysis. Computers, Environment and Urban Systems. 2017;64: 30-41. Available from: https://www.sciencedirect.com/science/article/pii/S0198971517300364 [Accessed 16th Oct. 2018].
Maher MJ, Birchall MC. Focus on planning for parking: A stochastic parking problem. Traffic Engineering & Control. 1975;16(5): 220-223. Available from: https://www.sciencedirect.com/science/article/pii/S0198971517300364 [Accessed 16th Oct. 2018].
Van der Goot D. A model to describe the choice of parking places. Transportation Research Part A: General. 1982;16(2): 109-115. Available from: https://www.sciencedirect.com/science/article/pii/0191260782900036 [Accessed 16th Oct. 2018].
Young W, Thompson RG, Taylor MA. A review of urban car parking models. Transport Reviews. 1991;11(1): 63-84. Available from: https://www.tandfonline.com/doi/abs/10.1080/01441649108716773 [Accessed 16th Oct. 2018].
Saltzman RM. An animated simulation model for analysing on-street parking issues. Simulation. 1997;69(2): 79-90. Available from: http://journals.sagepub.com/doi/abs/10.1177/003754979706900201 [Accessed 16th Oct. 2018]
Thompson RG, Richardson AJ. A parking search model. Transportation Research Part A: Policy and Practice. 1998;32(3): 159-170. Available from: https://www.sciencedirect.com/science/article/pii/S0965856497000050 [Accessed 16th Oct. 2018].
Shoup DC. The trouble with minimum parking requirements. Transportation Research Part A: Policy and Practice. 1999;33(7): 549-574. Available from: https://www.sciencedirect.com/science/article/pii/S0965856499000075 [Accessed 16th Oct. 2018].
Tam ML, Lam WH. Maximum car ownership under constraints of road capacity and parking space. Transportation Research Part A: Policy and Practice. 2000;34(3): 145-170. Available from: https://www.sciencedirect.com/science/article/pii/S0965856498000706 [Accessed 16th Oct. 2018].
Wong SC, Tong CO, Lam WC, Fung RY. Development of parking demand models in Hong Kong. Journal of Urban Planning and Development. 2000;126(2): 55-74. Available from: https://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9488(2000)126:2(55) [Accessed 16th Oct. 2018].
Thompson RG, Takada K, Kobayakawa S. Optimisation of parking guidance and information systems display configurations. Transportation Research Part C: Emerging Technologies. 2001;9(1): 69-85. Available from: https://www.sciencedirect.com/science/article/pii/S0968090X00000310 [Accessed 16th Oct. 2018].
D’Acierno L, Gallo M, Montella B. Optimisation models for the urban parking pricing problem. Transport Policy. 2006;13(1): 34-48. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0967070X05001034 [Accessed 16th Oct. 2018].
Imae J, Yoshimura K, Zhai G, Kobayashi T. Real-time optimisation for parallel-parking control of fourwheeled vehicles. International Journal of Modelling, Identification and Control. 2009;6(3): 255-262. Available from: https://www.inderscienceonline.com/doi/abs/10.1504/IJMIC.2009.024465 [Accessed 16th Oct. 2018].
Yasunobu S, Murai Y. Parking control based on predictive fuzzy control. Proceedings of the 3rd International Fuzzy Systems Conference, 26-29 June 1994, Orlando, FL, USA. IEEE; 1994. p. 1338-1341. Available from: https://ieeexplore.ieee.org/abstract/document/343618 [Accessed 16th Oct. 2018].
Tsai MT, Chu CP. Evaluating parking reservation policy in urban areas: An environmental perspective. Proceedings of the 9th International Conference of Eastern Asia Society for Transportation Studies, 2011. Eastern Asia Society for Transportation Studies; 2011. p. 272. Available from: https://www.jstage.jst.go.jp/article/eastpro/2011/0/2011_0_272/_article/-char/ja/ [Accessed 16th Oct. 2018].
Wang Y, Zhu X. A robust design of hybrid fuzzy controller with fuzzy decision tree for autonomous intelligent parking system. Proceedings of the American Control Conference (ACC), 4-6 June 2014, Portland, OR, USA. IEEE; 2014. p. 5282-5287. Available from: https://ieeexplore.ieee.org/abstract/document/6859439 [Accessed 16th Oct. 2018].
Caicedo F, Blazquez C, Miranda P. Prediction of parking space availability in real time. Expert Systems with Applications. 2012;39(8): 7281-7290. Available from: https://www.sciencedirect.com/science/article/pii/S0957417412001042 [Accessed 16th Oct. 2018].
Matoba S, Yasunobu S. A cooperative parking system based on fuzzy intention. Proceedings of the Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), 3-6 Dec. 2014, Kitakyushu, Japan. IEEE; 2014. p. 573-578. Available from: https://ieeexplore.ieee.org/abstract/document/7044749 [Accessed 16th Oct. 2018].
Sun DJ, Cheng JM, Zhang Y. An AHP-Fuzzy comprehensive evaluation model for parking lots in urban CBD area. Advances in Transportation Studies. 2015;37: 141-156.
Pamucar D, Ćirović G. Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering. 2018;1(1): 13-37. Available from: http://www.dmame.org/index.php/dmame/article/view/1 [Accessed 28th Feb. 2019].
Pamučar D, Vasin L, Atanasković P, Miličić M. Planning the City Logistics Terminal Location by Applying the Green-Median Model and Type-2 Neurofuzzy Network. Computational Intelligence and Neuroscience. 2016. Available from: https://www.hindawi.com/journals/cin/2016/6972818/abs/ [Accessed 28th Feb. 2019].
Munjal P, Pahl J. An analysis of the Boltzmann-type statistical models for multi-lane traffic flow. Transportation Research. 1969;3(1): 151-163.
HCM 2010. Transportation Research Board, National Research Council, Washington, DC. Available from: https://trrjournalonline.trb.org/doi/book/10.5555/9780309160773 [Accessed 16th Oct. 2018].
Jang JS. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics. 1993;23(3): 665-685. Available from: https://ieeexplore.ieee.org/abstract/document/256541 [Accessed 16th Oct. 2018].
Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science. Report No. 164453, 1985. Available from: http://www.dtic.mil/docs/citations/ADA164453 [Accessed 16th Oct. 2018].
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
