Type-2 Fuzzy Rule-Based Model of Urban Metro Positioning Service

In the last few years there has been a growing interest in route building oriented mobile applications with the following features of navigation and sending timely notifications about arrival. Despite the large body of existing knowledge on navigational services, there has been an important issue relative to positioning accuracy. The paper discusses a possible solution to comparison problem, which is linked to the determination of the closeness to destination metro station through finding a difference between user’s current coordinates and fixed-point coordinates. With this end in view, fuzzy logic approach is used to develop Routes Recommender System (RRS) that utilizes linguistic variables to express the vague and uncertain term ‘closeness to…’. The paper provides detailed explanation of each variable considered in the fuzzy inference system (FIS), set of fuzzy rules in line with graphical representation of system’s output. Based on Mamdani model, we propose a set of test cases to check maintainability of the model and provide a description about received results. At a later time, an Android-based mobile application aimed at public transport route building will be developed whose notification system will be based on our model`s implementation presented. It should be emphasized that the paper examines potentials of the modeling approach based on interval type-2 fuzzy sets (IT2FS) that attract much attention these days in various research studies and conventional Mamdani fuzzy inference system (MFIS) as applied to real and rather topical problem. The significance of developing such models may be of a high demand for appropriate representation of factors that are inherently vague and uncertain. Hence, this study may also contribute to future research on similar topics.

positioning service, mobile applications, fuzzy modeling, GPS, WiFi, cellular networks, public transport, interval type-2 fuzzy sets, fuzzy inference system, fuzzy matching of coordinates, uncertainty, fuzziness

1. Chen C-Y., Yang J.-P., Tseng G.-J., Wu Y.-H. and Hwang R.-C. An Indoor Positioning Technique Based on Fuzzy Logic, in Proc. International Multi Conference of Engineers and Computer Scientists (IMECS), 2010, pp. 854-857.

2. Teuber A. and Eissfeller B. WLAN Indoor Positioning Based on Euclidean Distances and Fuzzy Logic, in Proc. Workshop on Positioning, Navigation and Communication (WPNC), 2006, pp. 159-168.

3. Yasunobu S., Miyamoto S. and Ihara H. A Fuzzy Control for Train Automatic Stop Control. Transactions of the Society of Instrument and Control Engineers, 2002, vol. E-2(1), pp. 1-9.

4. Klir G.J. and Wierman M. Uncertainty Formalizations. In: Uncertainty-Based Information. Elements of Generalized Information Theory, ser. Studies in Fuzziness and Soft Computing (#15), 2nd ed., Physica Verlag, Germany, 1999, 168 p.

5. Arigela L., Veerendra P., Anvesh S. and Hanuman K. Mobile Phone Tracking & Positioning Techniques. Int. Journal of Innovative Research in Science, Engineering and Technology, 2012, vol.2, pp. 906-913.

6. Gps.gov, “Official U.S. Government Information About the GPS and Related Topics, GPS Accuracy”, 2017. [Online resource]. Available: http://www.gps.gov/systems/gps/performance/accuracy/ [Accessed 27-Feb-2017].

7. Zeimpekis V., Kourouthanassis P. E. and Giaglis G. M., Mobile and Wireless Positioning Technologies, in UNESCO Encyclopedia of Life Support Systems (EOLSS), vol. 6.108, EOLSS Publishers, France, 2007, [http://www.eolss.net].

8. Mosmetro.ru, “Metropoliten v tsifrakh”, 2017. [Online resource]. Available: http://mosmetro.ru/press/metropoliten-v-tsifrakh/ [Accessed 28-Jan-2017] (in Russian).

9. Nashemetro.ru, “Metro v tsifrakh.”, 2017. [Online]. Available: http://nashemetro.ru/facts.shtml [Accessed 13-Jan-2017] (in Russian).

10. Mendel J.M., Hagras H., Tan W.-W., et al. Introduction to Type-2 Fuzzy Logic Control. Theory and Applications (IEEE Press Series on Computational Intelligence), Wiley-IEEE Press, Piscataway, 2014, 376 p.

11. En.wikipedia.org, “Moscow Metro”, 2017. [Online resource]. Available: https://en.wikipedia.org/wiki/Moscow_Metro [Accessed 25-Jan-2017].

12. Mendel J.M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall PTR, Englewood Cliffs, 2001, 576 p.

13. Karnik N.N., Mendel J.M. Type-2 Fuzzy Logic Systems: Type-Reduction, in Proc. IEEE Int. Conference on Systems, Man, and Cybernetics, 1998, pp. 2046-2051.

14. Mendel J.M. Interval Type-2 Fuzzy Logic Systems and Perceptual Computers: Their Similarities and Differences. In: Sadeghian A., Mendel J., Tahayori H. (eds) Advances in Type-2 Fuzzy Sets and Systems. Studies in Fuzziness and Soft Computing, vol. 301. Springer, New York, 2013, pp. 3-18, doi: 10.1007/978-1-4614-6666-6_1.