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An intelligent vehicle detection management model for parking spaces

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Today, technology has transformed humans’ lives in all areas. Technology can be found in everyday life in the form of smart factories, smart cities and smart rooms. Thus, smart systems and devices are having a great effect upon human activities. Together with improving technology, in the last 50 years, the human population has seen an expeditious and substantial increase. The number of cars has also substantially increased and, as a result, parking spaces and car park have become more important. Areas in which to park cars have become wider in scope, including inside huge buildings; thus, the management of them become more difficult and complicated. Being able to discover an empty parking space as soon as it is needed is vital for both driver and multilevel parking garage owner, especially for the former, in order to avoid losing him/her time and money in a crowded city. Thus, we designed a smart car parking system, which is managed by Arduino control. Our main aim includes reducing time loss and the amount of fuel consumed while trying to find a parking space. By reducing the amount of consumed fuel, it will be possible to reduce air pollution levels, together with minimizing the negative impact on domestic finances caused by car usage.
Rocznik
Tom
Strony
35--43
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
  • Faculty of Engineering, Istanbul University, Engineering Science Department, Avcilar, Istanbul, Turkey
autor
  • Faculty of Engineering, Istanbul University, Computer Engineering Department, Avcilar, Istanbul, Turkey
autor
  • Faculty of Engineering, Istanbul University, Computer Engineering Department, Avcilar, Istanbul, Turkey
Bibliografia
  • 1. Jaiswal Raj K., C.D. Jaidhar. 2016. “Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter”. Wireless Networks: 1- 16.
  • 2. Silver S.D. 2017. “Multivariate methodology for discriminating market segments in urban commuting”. Public Transport. DOI 10.1007/s12469-017-0169-8.
  • 3. Gillen David W. 1978. “Parking policy, parking location decisions and the distribution of congestion”. Transportation 7(1), 69-85.
  • 4. Liao F., T. Arentze, E. Molin, W. Bothe, H. Timmermans. 2017. “Effects of land-use transport scenarios on travel patterns: a multi-state supernetwork application”. Transportation 44: 1-25.
  • 5. Dash Sarojeet, V. Vasudevan, S. Kumar Singh. 2016. “A piecewise linear multinomial logit model of private vehicle ownership behaviour of Indian households”. Transportation in Developing Economies 2: 17.
  • 6. Baratian-Ghorghi Fatemeh, H. Zhou. 2015. “Investigating women’s and men’s propensity to use traffic information in a developing country”. Transportation in Developing Economies 1: 11-19.
  • 7. Jiang N., C. Xie, J.C. Duthie, T.S. Waller. 2014. “A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows”. EURO Journal on Transportation and Logistics 3: 55-92.
  • 8. Ruan J.-M., B. Liu, H. Wei, Y. Qu, N. Zhu, X. Zhou. 2016. “How many and where to locate parking lots? A space-time accessibility-maximization modeling framework for special event traffic management”. Urban Rail Transit 2: 59-70.
  • 9. Kotelnikova N., F. Leurent. 2016. “Parking equilibrium along the street.” European Transport Research Review 8: 24.
  • 10. Cao Y., Z.Z. Yang, Z.Y. Zuo. 2017. “The effect of curb parking on road capacity and traffic safety”. European Transport Research Review 9: 4.
  • 11. Hössinger R., P. Widhalm, M. Ulm, K. Heimbuchner, E. Wolf, R. Apel, T. Uhlmann. 2014. “Development of a real-time model of the occupancy of short-term parking zones”. International Journal of Intelligent Transportation Systems Research 12: 37-47.
  • 12. Bulan O., R.P. Loce, W. Wu, Y.R. Wang, E.A. Bernal, Z. Fan. 2013. “Video-based real-time on-street parking occupancy detection system”. Journal of Electronic Imaging 22: DOI: 10.1117/1.JEI.22.4.041109.
  • 13. Ogiela L., R. Tadeusiewicz, M. Ogiela. 2006. “Cognitive analysis in diagnostic DSS-type IT systems”. In Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC 2006). 25-29 June 2006. Zakopane, Poland. Book Series: Lecture Notes in Computer Science Vol. 4029: 962-971.
  • 14. Ogiela L., R. Tadeusiewicz, M. Ogiela. 2006. “Cognitive computing in intelligent medical pattern recognition systems”. In D.S. Huang, K. Li, G.W. Irwin, eds., International Conference on Intelligent Computing (ICIC). 16 August 2006. Kunming, China. Book Series: Lecture Notes in Control and Information Sciences Vol. 344: 851- 856.
  • 15. Ogiela M., R. Tadeusiewicz, L. Ogiela. 2005. “Intelligent semantic information retrieval in medical pattern cognitive analysis”. In O. Gervasi, M.L. Gavrilova, V. Kumar et al., eds., International Conference on Computational Science and Its Applications (ICCSA 2005) Vol. 4. 9-12 May 2005. Singapore. Book Series: Lecture Notes in Computer Science Vol. 3483: 852-857.
  • 16. Tadeusiewicz R., L. Ogiela, M. Ogiela. 2008. “The automatic understanding approach to systems analysis and design”. International Journal of Information Management 28(1): 38-48.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-89c40f93-1b61-4cfd-8a99-b4b58fd0db63
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