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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Raihan Iqbal Ramadhan
Universitas Janabadra, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production Muhamad Willdan; Raihan Iqbal Ramadhan; Nindyo Cahyo Kresnanto; Wika Harisa Putri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 1, April 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss1.art6

Abstract

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.
Co-Authors Muhamad Willdan Nindyo Cahyo Kresnanto Putri, Wika Harisa