DEVELOPMENT OF A SYSTEM FOR ASSESSING PASSENGER TRAFFIC BASED ON FUZZY LOGIC

Keywords: passenger traffic, decision support, fuzzy mathematical model, degree of confidence in the attractiveness of passenger traffic, software

Abstract

A methodology has been developed for assessing public transport passenger traffic in the city. A mathematical model based on fuzzy logic is presented. The main criteria for assessing the attractiveness of passenger traffic are: the interval between vehicles, technical condition of the vehicle, route length, time of day. In the mathematical model, all input linguistic variables and output variable, their terms and membership functions are described. A fragment of a fuzzy knowledge base presented in the form of production rules is presented. At the exit, the dispatcher receives an output variable – the degree of confidence in the attractiveness of the route. Based on this assessment, the dispatcher can make a number of necessary changes to improve the functioning of the route. The software is implemented as a web service. This software will be convenient for dispatchers to use for planning public transport routes. Fifteen selected routes were taken for research, which are the most popular in the city. These routes were proposed for evaluation by three controllers. The results obtained from dispatchers were compared with the results of the fuzzy inference implemented in the software. The main advantage of using this software product is the ability to build a dynamic schedule based on the analysis of the dispatcher. This, in turn, will allow passengers to receive a better transportation service within the city

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Author Biography

Olha Pronina, State Higher Educational Institution "Priazov State Technical University"

PhD, Associate Professor

Department of Computer Science

References

Mei, L., Jing, L., Shu, K. (2011). Passenger travel choice prediciton based on fuzzy logic. Proceedings of the 13th International Conference on Enterprise Information Systems. doi: https://doi.org/10.5220/0003477901630166

Jassbi, J., Makvandi, P., Ataei, M., Sousa, P. A. C. (2011). Soft system modeling in transportation planning: Modeling trip flows based on the fuzzy inference system approach. African Journal of Business Management, 5 (2), 505–514.

Kompil, M., Celik, H. M. (2013). Modelling trip distribution with fuzzy and genetic fuzzy systems. Transportation Planning and Technology, 36 (2), 170–200. doi: https://doi.org/10.1080/03081060.2013.770946

Yin, D. (2018). Research on Fuzzy Comprehensive Evaluation of Passenger Satisfaction in Urban Public Transport. Modern Economy, 09 (03), 528–535. doi: https://doi.org/10.4236/me.2018.93034

Huang, H. B. (2014). The Research on Changsha City Bus Passenger Satisfaction Evaluation by AHP-Fuzzy Comprehensive Evaluation. Central South University of Forestry and Technology.

Sharma, B., Kumar Katiyar, V., Kumar Gupta, A. (2014). Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days. International Journal of Computer Applications, 107 (17), 1–6. doi: https://doi.org/10.5120/18840-0026

Hryhorova, T. M., Davidich, Yu. O., Dolia, V. K. (2015). Determination of the factors influencing the choice of the passenger kind regional transport. Visnyk Natsionalnoho tekhnichnoho universytetu "KhPI". Seriya: Mekhaniko-tekhnolohichni systemy ta kompleksy, 21, 29–37.

Davidich, N. (2016). Quality assessment in projects of public passenger transport. Suchasni tekhnolohiyi v mashynobuduvanni ta transporti, 1 (5), 63–67.

Tamalika, C. (2019). Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. John Wiley & Sons. doi: https://doi.org/10.1002/9781119544203

Mamdani, E. (1977). Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Transactions on Computers, C-26 (12), 1182–1191. doi: https://doi.org/10.1109/tc.1977.1674779


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Published
2020-11-30
How to Cite
Pronina, O. (2020). DEVELOPMENT OF A SYSTEM FOR ASSESSING PASSENGER TRAFFIC BASED ON FUZZY LOGIC. Technology Transfer: Fundamental Principles and Innovative Technical Solutions, 18-21. https://doi.org/10.21303/2585-6847.2020.001528
Section
Computer Sciences