Abstract
The work considers the urgent task of collecting and analyzing information received during the work of the taxi order service. The data obtained by the taxi service can be easily represented by different time series. Particular attention is also paid to the use of neural networks to solve the predicting problem. The relevance of using neural networks in comparison with statistical models is substantiated. The special software used allows one’s to collect information on the operation of the service in a variety of SQL tables. Particular attention is paid to existing programming languages that allow to implement data mining processes. The strengths and weaknesses are highlighted for this languages. Based on the accumulated data on the numbers of taxi service orders, the algorithms for predicting the operation of a taxi service were studied using both neural networks and mathematical models of random processes. Comparative predicting characteristics are obtained, variances of predicting errors are found. The results of construction using autoregressive and doubly stochastic models, as well as using fuzzy logic models, are presented. It is shown that the use of neural networks provides smaller errors in predicting the number of taxi service orders.
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References
Schuh, G., Reinhart, G., Prote, J., Sauermann, F., Horsthofer, J., Oppolzer, F., Knoll, D.: Data mining definitions and applications for the management of production complexity. Procedia CIRP 81, 874–879 (2019)
Oluwaseun, A., Chaubey, M.: Data mining classification techniques on the analysis of students performance (2019). https://doi.org/10.11216/gsj.2019.04.19671
Bharati, M., Ramageri, M.: Data mining techniques and applications. Indian J. Comput. Sci. Eng. 1, 301–305 (2010)
Andriyanov, N.A., Gavrilina, Yu.N.: Image models and segmentation algorithms based on discrete doubly stochastic autoregressions with multiple roots of characteristic equations. In: CEUR Workshop Proceedings, vol. 2076, pp. 19–29 (2018)
Hamuda, E., Glavin, M., Jones, E.: A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184–199 (2016)
Andriyanov, N.A., Vasil’ev, K.K., Dement’ev, V.E.: Investigation of the filtering and objects detection algorithms for a multizone image sequence. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, pp. 7–10 (2019)
Umang, P.: NYC taxi trip and fare data analytics using BigData (2015). https://doi.org/10.13140/RG.2.1.3511.0485
Andriyanov, N.A., Sonin, V.A.: Using mathematical modeling of time series for forecasting taxi service orders amount. In: CEUR Workshop Proceedings, vol. 2258, pp. 272–462 (2018)
Guo, J.: Analysis and comparison of Uber, Taxi and Uber request via Transit. JISC, p. 29 (2018)
Danilov, A.N., Andriyanov, N.A., Azanov, P.T.: Ensuring the effectiveness of the taxi order service by mathematical modeling and machine learning. In: Journal of Physics: Conference Series, vol. 1096 (2018). https://doi.org/10.1088/1742-6596/1096/1/012188
Deri, J., Moura, J.: Taxi data in New York city: A network perspective, pp. 1829–1833 (2015). https://doi.org/10.1109/acssc.2015.7421468
Azanov, P.T., Danilov, A.N., Andriyanov, N.A.: Development of software system for analysis and optimization of taxi services efficiency by statistical modeling methods. In: CEUR Workshop Proceedings, vol. 1904, pp. 232–238 (2017). https://doi.org/10.18287/1613-0073-2017-1904-232-238
Vasiliev, K.K., Dementyiev, V.E., Andriyanov, N.A.: Using probabilistic statistics to determine the parameters of doubly stochastic models based on autoregression with multiple roots. In: Journal of Physics: Conference Series, vol. 1368, pp. 1–8 (2019). https://doi.org/10.1088/1742-6596/1368/3/032019
Andriyanov, N.A., Dementyiev, V.E.: Determination of borders between objects on satellite images using a two-proof doubly stochastic filtration. In: Journal of Physics: Conference Series, vol. 1353, pp. 1–7 (2019). https://doi.org/10.1088/1742-6596/1353/1/012006
Vasiliev K.K., Andriyanov N.A.: Synthesis and analysis of doubly stochastic models of images. In: CEUR Workshop Proceedings, vol. 2005, pp. 145–154 (2017)
Andriyanov, N.A., Dementiev, V.E., Vasiliev, K.K.: Developing a filtering algorithm for doubly stochastic images based on models with multiple roots of characteristic equations. Pattern Recogn. Image Anal. 29(1), 10–20 (2019). https://doi.org/10.1134/S1054661819010048
Andriyanov, N.A., Sluzhivyi, M.N.: Solution for the problem of the parameters identification for autoregressions with multiple roots of characteristic equations. In: CEUR Workshop Proceedings, vol. 2391, pp. 79–85 (2019)
Wood, G., Batt, J., Appelboam, A., Harris, A., Wilson, M.: Exploring the impant of expertise, clinical history, and visual search on electrocardiogram interpretation. Med. Decis. Making 34(1), 75–83 (2014)
Yager, R., Filev, D.: Essentials of Fuzzy Modeling and Control, p. 387. Wiley, New York (1984)
Wood, G., Knapp, K.M., Rock, B., Cousens, C., Roobotton, C., Wilson, M.: Visual expertise in detecting and diagnosing skeletal fractures. Skeletal Radiol. 42(2), 165–172 (2013)
https://bigdata-madesimple.com/top-8-programming-languages-every-data-scientist-should-master-in-2019/. Accessed 28 Sept 2019
http://julialang.org/teaching/. Accessed 28 Sept 2019
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Appendix
Appendix
Before implementing the data mining algorithms in a specific programming language, it is worth to consider the main trends in this area [21].
A huge number of tasks today are associated with the processing of experimental data, or with mathematical modeling of some real process. These tasks are successfully solved by such hardware as personal computers and, in some cases, even computing clusters and supercomputers. In the software part, there are many programming languages that can be used for numerical calculation. Compared to general-purpose languages, they provide a simple (often intuitive) program syntax, as well as a large library of specialized functions. All of them are interpretable, which speeds up the implementation and debugging of algorithms, but negatively affects the speed of programs. These include Matlab, with its implementations such as Octave and Scilab. These programs operate perfectly with matrix calculations. Python is also gradually gaining popularity in the scientific community, along with the optional NumPy and SciPy modules.
Unfortunately, increasing the speed of programs requires moving the code to one of the traditionally used static languages (C/C++, Fortran). Obviously, the need to rewrite the program creates additional difficulties for the researcher.
Let consider some already proven tools and relatively new languages, such as Julia [22] in more detail. The analysis shows that the following table (see Table 2) can be compiled quite fully characterizing the studied programming languages.
Thus, an analysis was performed on programming languages that can now be successfully applied to data processing. It is important to understand what tasks need to be solved in order to choose the necessary language. After all, some languages have specificity and versatility, and some languages have properties of convenience and efficiency. Nevertheless, for our research on predicting the number of taxi service orders, we will choose the languages Matlab (for implementing mathematical models of random processes) and Julia (for implementing models of fuzzy logic).
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Andriyanov, N.A. (2020). Development of Prediction Methods for Taxi Order Service on the Basis of Intellectual Data Analysis. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_49
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