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Septia Lutfi
STMIK Himsya
Electrical & Electronics Engineering

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Prediction of passenger train using fuzzy time series and percentage change methods Solikhin Solikhin; Septia Lutfi; Purnomo Purnomo; Hardiwinoto Hardiwinoto
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.2822

Abstract

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.
A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model Solikhin Solikhin; Septia Lutfi; Purnomo Purnomo; Hardiwinoto Hardiwinoto
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3518

Abstract

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.
Co-Authors Hardiwinoto Hardiwinoto Hardiwinoto Hardiwinoto Purnomo . Purnomo Purnomo Solikhin Solikhin