Abstract
The modern data collection and analysis methods enhance reasonable and effective data-driven decision making in transport planning and have significantly expanded the scope of potential data applications. But if the goal is to use data in transport models for evaluating and predicting, the quality of data becomes crucial.
This research is focused on data pre-processing issues such as data understanding, data exploring, and data transformation as an important part of data analysis life cycle rather than transformation models themselves. These phases involve many different tasks and many of the data preparation activities are routine, tedious, and time consuming.
In order to resolve this problem the data preparation framework for Markov-modulated linear regression model, considering the limitations and assumptions, was developed. This kind of regression model can be used for public transport passenger flow prediction or other transport planning tasks and suggests that the model parameters vary randomly in accordance with the external environment. The developed framework is applied on data concerning Riga tram route trip validation captured by e-ticket system “E-talons” and the Latvian Environment, Geology and Meteorology Centre database. R software is used in conjunction with a set of libraries.
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Acknowledgement
This work was financially supported by the specific support objective activity 1.1.1.2. “Post-doctoral Research Aid” (Project id. N. 1.1.1.2/16/I/001) of the Republic of Latvia, funded by the European Regional Development Fund. Nadezda Spiridovska research project No. 1.1.1.2/VIAA/1/16/075 “Non-traditional regression models in transport modelling”.
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(Yatskiv), I.J., Spiridovska, N. (2019). Data Preparation Framework Development for Markov-Modulated Linear Regression Analysis. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-12450-2_17
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