Holt-Winters Forecasting Method That Takes into Account the Effect of Eid

An appropriate time series model for predicting passenger data of Djalaluddin Gorontalo Airport, the interval between 2003 - 2017 with the multiplicative of Holt’s-Winters’ exponential smoothing as Y ^ t + p = ( L t + p T t ) S t − 12 + p , where for time t, the original exponential smoothing data, L t = 0.2 Y t S t − 12 + 0.8 ( L t − 1 + T t − 1 ) , smoothing trend patterns Tt = 0.2(Lt − L t−1) + 0.8T t−1 and smoothing seasonal patterns, S t = 0.2 Y t L t + 0.8 S t − 12 , so the smoothing parameter used are α = β = γ = 0.2. After several treatments, the mean square deviation (MSD) is 3287241 for arrival passenger data and MSD is 2490279 for passenger departure data with a seasonal length of 12 by do not event-based. The next, the MSD is 1334585 for arrival passenger data and MSD is 1433867 for passenger departure data with a seasonal length of 12 by Eid al-Fitr event-based, the MSD is 1259600 for arrival passenger data and MSD is 1252548 for passenger departure data with a seasonal length of 12 by Eid al-Adha event-based.

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Forecasting travel patterns during COVID-19 period using Community Mobility Report Case study: Bangka Belitung Province

Safitri

Amelia

2021

IOP Conf. Ser.: Earth Environ. Sci.

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The pandemic of COVID-19 has caused many changes and one of the changes is in people’s travel patterns. Travel patterns have potentially related to environmental factors. The patterns in an urban environment can affect the liveability increase and the planning in urban areas. The change of travel pattern data is necessary for transportation planners and policymakers in providing safe transportation during the pandemic. Therefore, forecasting travel patterns is particularly essential to plan a better environment to reduce the spread of COVID-19. This study tries to forecast travel patterns during the COVID-19 period in Province of Bangka Belitung using the Community Mobility Report provided by Google. In this study, a time series model is necessary to predict future mobility. The model used in this research is exponential smoothing. The results find that the prediction of travel patterns for each trip destination experiences a fluctuating trend. The prediction for going to retail and recreation, grocery stores and pharmacies, parks, and transit stations tends to increase. Otherwise, the prediction of travel patterns for going to workplaces and residential decreases. Furthermore, the results of this study are feasible to plan a better environment to reduce the spread of COVID-19.

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Holt-Winters Forecasting Method That Takes into Account the Effect of Eid

Cited by 5 publications

References 5 publications

Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction

Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction

Djalaluddin Gorontalo Airport Passenger Data Forecasting with Holt’s-Winters’ Exponential Smoothing Multiplicative Event-Based Method

Forecasting travel patterns during COVID-19 period using Community Mobility Report Case study: Bangka Belitung Province

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