Implementasi Regresi Linear Berganda dalam Prediksi Waktu Tiba Bus Transjakarta Menuju Destinasi Akhir

Transjakarta, the primary public transportation system in Jakarta, plays a strategic role in meeting the growing mobility needs of urban residents. This study aims to implement multiple linear regression to predict bus arrival times at final destinations using simulated transaction data that includes tap-in and tap-out information, bus routes, travel times, as well as additional variables such as traffic conditions, route distances, and operational hours. The analysis results indicate that the predictive model developed has high accuracy, with traffic conditions and route distances identified as significant factors. The implementation of this model has the potential to assist Transjakarta operators in improving route management efficiency, reducing passenger wait times, and providing more accurate arrival time estimates through supporting applications. This research also lays the foundation for developing data-driven prediction systems for public transportation management.

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Publication Details

Journal
Journal of Digital Ecosystem for Natural Sustainability
Published
2024-12-31
DOI
https://doi.org/10.63643/jodens.v4i2.258
Primary Topic
Urban Transport Systems Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Implementasi Regresi Linear Berganda dalam Prediksi Waktu Tiba Bus Transjakarta Menuju Destinasi Akhir

Duandi, Vivie Triyanti, Steven Tang
Journal of Digital Ecosystem for Natural Sustainability
Urban Transport Systems Analysis
article

Implementasi Regresi Linear Berganda dalam Prediksi Waktu Tiba Bus Transjakarta Menuju Destinasi Akhir

Duandi, Vivie Triyanti, Steven Tang
article en

Abstract

Transjakarta, the primary public transportation system in Jakarta, plays a strategic role in meeting the growing mobility needs of urban residents. This study aims to implement multiple linear regression to predict bus arrival times at final destinations using simulated transaction data that includes tap-in and tap-out information, bus routes, travel times, as well as additional variables such as traffic conditions, route distances, and operational hours. The analysis results indicate that the predictive model developed has high accuracy, with traffic conditions and route distances identified as significant factors. The implementation of this model has the potential to assist Transjakarta operators in improving route management efficiency, reducing passenger wait times, and providing more accurate arrival time estimates through supporting applications. This research also lays the foundation for developing data-driven prediction systems for public transportation management.

Journal of Digital Ecosystem for Natural SustainabilityVol. 4(2)
Universal University (RU)
Openalex Percentile: Top 68%
Urban Transport Systems Analysis
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