<![CDATA[Efficient urban mobility is essential to support transportation planning and policy. However, traditional methods are often limited in data resolution, lacking the ability to describe passenger movement dynamics in detail. This study aims to analyze passenger mobility patterns using high-resolution tap-in/tap-out data from the closed-loop LRT system in Jakarta during January-February 2025. The methods used include constructing an origin-destination (OD) matrix based on 185,512 trip records, as well as temporal and spatial analysis of passenger flows. The results showed the existence of peak hour patterns on weekdays (07.00-09.00 and 17.00-19.00), trip spikes on weekends and holidays (14.00-18.00), and high flow concentrations at interchange stations such as Velodrome and North Boulevard. While data from the closed system allows for accurate trip tracking, potential data gaps due to technical errors or user behavior remain a concern for long-term analysis. The findings suggest that high-resolution smart card data can provide operationally relevant insights for short-term decision-making, such as schedule adjustments or fleet allocation. However, for long-term strategic planning, integration with predictive models and other planning tools remains necessary. This research fills a gap in the literature by showing that even limited-duration datasets can be leveraged to effectively support data-driven transportation management.]]>
Copyrights © 2025
