Future Aspects of Intelligent Car Parking Based on Internet of Things


  • Muzhir Shaban Al-Ani Department of Information Technology, College of Science and Technology, University of Human Development, KRG, Iraq
  • Zana Azeez Kakarash Department of Information Technology, College of Science and Technology, University of Human Development, KRG, Iraq http://orcid.org/0000-0002-7469-2914




Intelligent Algorithm, Car Parking, Real Time System, IoT


Nowadays, the crowded of cars leads to big challenges in many crowded cities. This leads to environmental pollution in addition to fuel consuming. In addition, it is very important to adapt all devices, vehicles, and objects to the environment of the internet of things (IoT). In this case, it is difficult to find the nearest and shortest suitable path for car parking place. This approach aims to minimize the time for finding the car parking as well as reducing the traffic congestion. The implemented approaches try to support the driver to find near suitable car parking. The implemented approach based on intelligent aspects to achieve the performance of car parking with the future environment of IoT. Localizing of the nearest car parking is an important issue in the future IoT.


[1] J. Gimeno, C. Portalés, I. Coma, M. Fernández and B. Martínez. Combining traditional and indirect augmented reality for indoor crowded environments. A case study on the Casa Batlló museum. Computers and Graphics, vol. 69, pp. 92-103, 2017.

[2] D.J.W. Barnaby, M.J. Rantala, E.F. Melo and R.C. Brooks. Beards and the big city: Displays of masculinity may be amplified under crowded conditions. Evolution and Human Behavior, vol. 38, no. 2, pp. 259-264, 2017.

[3] J. Rappaport. A productivity model of city crowdedness. Journal of Urban Economics, vol. 63, no. 2, pp. 715-722, 2008.

[4] A. Alahi, V. Ramanathan and L. Fei-Fei. Chapter 6: Tracking Millions of Humans in Crowded Spaces, Book Chapter, Group and Crowd Behavior for Computer Vision, 2017. pp. 115-135.

[5] M. Grove. Population density, mobility, and cultural transmission. Journal of Archaeological Science, vol. 74, pp. 75-84, 2016.

[6] D. de la Croix and P.E. Gobbi. Population density, fertility, and demographic convergence in developing countries. Journal of Development Economics, vol. 127, pp. 13-24, 2017.

[7] A. Gupta and V. Rastogi. Effects of various road conditions on dynamic behaviour of heavy road vehicle. Procedia Engineering, vol. 144, pp. 1129-1137, 2016.

[8] C. Men, R. Liu, F. Xu, Q. Wang and Z. Shen. Pollution characteristics, risk assessment, and source apportionment of heavy metals in road dust in Beijing, China. Science of the Total Environment, vol. 612, pp. 138-147, 2008.

[9] O. Stepanchuk, A. Bieliatynskyi, O. Pylypenko and S. Stepanchuk. Surveying of traffic congestions on arterial roads of Kyiv city. Procedia Engineering, vol. 187, pp. 14-21, 2017.

[10] J. Lizbetin and L. Bartuska. The influence of human factor on congestion formation on urban roads. Procedia Engineering, vol. 187, pp. 206-211, 2017.

[11] T.T.M. Thanh and H. Friedrich. Legalizing the illegal parking, a solution for parking scarcity in developing countries. Transportation Research Procedia, vol. 25, pp. 4950-4965, 2017.

[12] R. Arnott and P. Williams. Cruising for parking around a circle. Transportation Research Part B: Methodological, vol. 104, pp. 357-375, 2017.

[13] M. Dodourova and K. Bevis. Networking innovation in the European car industry: Does the open innovation model fit? Transportation Research Part A: Policy and Practice, vol. 69, pp. 252-271, 2014.

[14] S. Cherubini, G. Iasevoli, and L. Michelini. Product-service systems in the electric car industry: Critical success factors in marketing. Journal of Cleaner Production, vol. 97, pp. 340-349, 2015.

[15] D. Zhu, L. Wang, J. Henaut and S. Beeby. Comparisons of energy sources for autonomous in-car wireless tags for asset tracking and parking applications. Procedia Engineering, vol. 87, pp. 783-786, 2014.

[16] J.H. Shin and H.B. Jun. A study on smart parking guidance algorithm. Transportation Research Part C: Emerging Technologies, vol. 44, 299-317, 2014.

[17] N. Levy and I. Benenson. GIS-based method for assessing city parking patterns. Journal of Transport Geography, vol. 46, pp. 220-231.

[18] S. Belloche. On-street parking search time modelling and validation with survey-based data. Transportation Research Procedia, vol. 6, pp. 313-324, 2015.

[19] S.A. Alkheder, M.M. Al Rajab and K. Alzoubi. Parking problems in Abu Dhabi, UAE toward an intelligent parking management system “ADIP: Abu Dhabi Intelligent Parking”. Alexandria Engineering Journal, vol. 55, no. 3, 2679-2687, 2016.

[20] Y. Atif, J. Ding, and M.A. Jeusfeld. Internet of things approach to cloud-based smart car parking. Procedia Computer Science, vol. 98, pp. 193-198, 2016.

[21] J. Bischoff and K. Nagel. Integrating explicit parking search into a transport simulation. Procedia Computer Science, vol. 109, pp. 881-886, 2017.

[22] P. Christiansen, N. Fearnley, J.U. Hanssen and K. Skollerud. Household parking facilities: Relationship to travel behaviour and car ownership. Transportation Research Procedia, vol. 25, pp. 4185-4195, 2017.

[23] D. Thomas and B.C. Kovoor. A genetic algorithm approach to autonomous smart vehicle parking system. Procedia Computer Science, vol. 125, pp. 68-76, 2018.

[24] M. Nourinejad, S. Bahrami and M.J. Roorda. Designing parking facilities for autonomous vehicles. Transportation Research Part B: Methodological, vol. 109, pp. 110-127, 2018.






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