<![CDATA[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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