Freight Demand Statistical Modeling: A Classification and Review
2017
M.P, Boile | M., Golias
Linear Regression is used as a prediction tool in transportation planning, traffic data analysis and safety.Many researchers have attempted to address several transportation issues using these types of models. Theaccuracy and stability of these models is mainly dependent on the size of the available data. Unlike otherscience fields, where sophisticated algorithms are used to deal with problems of small datasets, the majorityof freight demand modeling relies on simple statistical techniques. Limitations of these modelingmethodologies, caused by their high dependence on data availability, and several assumptions that need tobe made can result in erroneous models. In cases in which limited data are available, more advancedalgorithms that can be legitimately used on small datasets should be applied. In this paper a description andclassification of algorithms and processes used for creating these types of models under limited data ispresented. To demonstrate the applicability of these algorithms, along with implementation problems,limitations, and the different performance measures, a case study is used. Different models are created andresults are presented and discussed.
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