Excursion Groups Flow Forecasting Based on Modified Random Forest

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MAGAZINE  №4 (99) August 2020

AUTHORS ASLAKHANOV A.R., PAVLOVA E.V.

CATEGORY  Information technologies in logistics and SCM Logistics service management Optimization and mathematical modelling Simulation modelling

 

ABSTRACT

In recent years, both in Russia and worldwide, there has been an annual increase in the number of museum visitors with the most popular exhibitions being visited by millions of people. In 2020, in the context of quarantine measures caused by the COVID-19 pandemic, the issue of managing museum visitors flows has become especially acute. If earlier museums throughput was limited by the maximum duration of possible evacuation from a museum building, exhibition space and the number of employees working with the visitors, in 2020, due to the observance of sanitary and epidemiological rules, the throughput of museums was further reduced. This determines the relevance of analytical solutions for museums as in order to manage visitor flows and adapt services to the high demand, it is necessary to have an effective forecasting model that takes into account the determinism of demand by a number of factors. The purpose of this paper is to develop a forecasting model for the number of excursion groups in specification museum-day-hour. A modification of random forest with the inclusion of more than 450 independent variables in the model is proposed as a forecasting method. The modification of the model relies on changing the mechanism for combining forecasts of trees in the forest in such a way that the weight of the tree in the model is inversely proportional to the measurement error of this tree. The proposed model is tested on the basis of data on more than 20,000 excursion groups of the State Russian Museum for the period 2018-2020. The proposed model showed high accuracy (36.6% WAPE and 0.5% BIAS).

 

 Electronic version

 Keywords:  forecasting machine learning random forest combining forecasts Python forecast museum Service service quality

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