Design and Implementation of Human Mobility Embedded System for Urban Planning of Smart City

Anwar Al-Khateeb

2017

Abstract

The mobility model will help us to simulate the movements of people more realistic as in real case. This paper developed human mobility system using random way point and random walk methods to model the individual movement of person in different places within area of Baghdad, Iraq. There are a lot of types of mobility model but we use Random Way Point (RWP) because it is simple, most common use and needs less memory and time for computation. The geographical information and maps of the cities are used to tell the person about the constraints and correct the direction of motion in different areas to generate more accurate data. Tested areas are quite different in style, structure, social, history and culture. The most similar thing between them is that they are both in continuously changing which is the one of biggest difficulties in designing the city. Al-Sadar city is one of biggest population city in Baghdad, Iraq. It has three millions people population. We choose 5000 users moves from Al-Sadar city to two different places in Baghdad: Al-Khadmia and Al-Zawra. Al-Khadmiia is old and religion place and Al-Zawra is biggest public garden in Baghdad. In our work, we find the best ways that connect Al-Sadar city with both places. We decide the best ways depending on distances and traffic between them. It gives good method to build smart transportation and smart city using mobility and traffic models. .This paper will help urban decision maker to suggest the analytical model for urban space and have a clear picture about the city: how can it change, how do the people move in the city, what are the problems and how can solve them to generate smart city.

References

  1. Vicente Casares-Giner_, Vicent Pla, and Pablo EscalleGarcía,” Mobility Models for Mobility Management”, Next Generation Internet, LNCS 5233, pp. 716-745, 2011.
  2. Ignacio Martinez-Arrue, Pablo Garcia-Escalle and Vicente Casares-Giner,” Location Management Based on the Mobility Patterns of Mobile Users”, Wireless Systems and Mobility in Next Generation Internet Lecture Notes in Computer Science Volume 5122, 2008, pp 185-200.
  3. Yu-Liang Tang, Chun-Cheng Lin, Yannan Yuan1 and Der-Jiunn Deng,” Dividing Sensitive Ranges Based Mobility Prediction Algorithm in Wireless Networks”, Tamkang Journal of Science and Engineering, Vol. 13, No. 1, pp. 107_115, 2010.
  4. Son, D., Helmy, A. and Krishnamachari, B., “The Effect of Mobility-Induced Location Errors on Geographic Routing in Mobile Ad Hoc Sensor Networks: Analysis and Improvement Using Mobility Prediction,” IEEE Transactions on Mobile Computing, Vol. 3, pp. 233_245 (2004).
  5. Mir, Z. H., Shrestha, D. M., Cho, G.-H. and Ko, Y.-B., Mobility Aware Distributed Topology Control for Mobile Multi-Hop Wireless Networks, in Proc. of ICOINS 2006, Vol. 3961 of LNCS. 2006, pp. 257_266.
  6. TAMÁS SZÁLKA, SÁNDOR SZABÓ, PÉTER FÜLÖP, ”Markov model based location prediction in wireless cellular networks”, Info-communication journal, 2009.
  7. Peppino Fazio, Salvatore Marano,” A New Markov-Based Mobility Prediction Scheme for Wireless Networks with Mobile Hosts” International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 8-11 July 2012.
  8. R. Chellappa Doss , A. Jennings, N. Shenoy, “A Review on Current Work in Mobility Prediction for Wireless Networks”, Proceedings Third Asian International Mobile Computing Conference, Bangkok, Thailand, 26-28 May 2004.
  9. J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, A performance comparison of multi-hop wireless ad hoc network routing protocols, in Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking (Mobicom98), ACM, October 1998.
  10. Berk Birand, Murtaza Zafer, Gil Zussman, Kang-Won Lee,” Dynamic Graph Properties of Mobile Networks under Levy Walk Mobility”, Eighth IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, 2011.
  11. Injong Rhee, Minsu Shin, Seongik Hong, “On the LevyWalk Nature of Human Mobility”, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 19, NO. 3, JUNE 2011.
  12. Francesco Calabrese, Giusy Di Lorenzo, C. Ratti, “Human Mobility Prediction based on Individual and Collective Geographical Preferences”, 2010.
  13. Matteo Leccardi,”Comparison of Three Algorithms for Lévy Noise Generation”, 2005.
  14. Jae-Hyung Jeon, Eli Barkai, Ralf M, etzler, ” Noisy continuous time random walks”, final report, 2013.
  15. Heiko Bauke,78 Parameter estimation for power-law distributions by maximum likelihood methods', August, 2007.
  16. Figure 3 Openstreet Map for for Al-Sadar City and AlZawra places in Baghdad, Iraq with parsed information using Matlab.
Download


Paper Citation


in Harvard Style

Al-Khateeb A. (2017). Design and Implementation of Human Mobility Embedded System for Urban Planning of Smart City . In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-241-7, pages 337-345. DOI: 10.5220/0006374103370345


in Bibtex Style

@conference{smartgreens17,
author={Anwar Al-Khateeb},
title={Design and Implementation of Human Mobility Embedded System for Urban Planning of Smart City},
booktitle={Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2017},
pages={337-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006374103370345},
isbn={978-989-758-241-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Design and Implementation of Human Mobility Embedded System for Urban Planning of Smart City
SN - 978-989-758-241-7
AU - Al-Khateeb A.
PY - 2017
SP - 337
EP - 345
DO - 10.5220/0006374103370345