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

Abstract

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.

Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production

“…A travel pattern is a typical behavior caused by numerous elements during travel as a collective attribute of travel [21]. Several previous studies have revealed what factors could influence travel patterns, including global health situations such as pandemics [22], [23], geographical conditions [24], socioeconomic factors [25], [26], as well as from socio-demographic factors [27]. Understanding people's travel patterns is essential for route planning.…”