Analisis Data Operasional Mrt Fase 1 terhadap Prediksi Jumlah Penumpang di Fase 2 - Aswinto, Aswinto; Azhar, Moh; Mardiaman, Mardiaman (Universitas Batanghari Jambi, 2025)
Urban congestion in rapidly growing cities like Jakarta necessitates efficient, data-driven transportation planning. As part of Indonesia’s efforts to modernize its urban mobility systems, the Jakarta Mass Rapid Transit (MRT) has emerged as a strategic initiative to reduce traffic dependency on private vehicles. While Phase 1 of MRT Jakarta has been operational since 2019, Phase 2 development requires a reliable forecasting framework to anticipate future passenger demand and support infrastructure planning. However, previous studies often rely on macroeconomic projections rather than empirical operational data, leaving a gap in predictive accuracy. This study aims to analyze the influence of MRT Jakarta phase 1 operational data on the prediction of passenger numbers in phase 2 as the basis for evidence-based transportation development planning using a hybrid approach: ARIMA forecasting and multiple linear regression modeling. Primary data were gathered from 125 MRT users via Likert-scale surveys, while secondary data were derived from MRT Jakarta’s operational reports from 2019–2023. Here we show that service quality and multimodal integration significantly increase ridership, while fare levels and poor accessibility negatively affect passenger volume. The ARIMA model, however, shows limited accuracy (R² = 0.164; MAPE = 62.225%), indicating high variability due to pandemic-induced fluctuations. In contrast, regression analysis explains 44.1% of ridership variation, offering a more actionable basis for forecasting. These findings suggest that MRT Phase 2’s success will depend not only on physical expansion but also on improved intermodal integration, user-centered service quality, and competitive fare strategies. The study contributes to transport planning by integrating quantitative modeling with user behavior insights for data-driven infrastructure scheduling.