Geovisualization of trajectory data using space-time cubes: a case study of crowdsourced traffic congestion data

Traffic congestion is a common issue, particularly during peak hours, holiday seasons, and religious holidays, with cities like Yogyakarta experiencing significant traffic challenges. According to INRIX Research, Yogyakarta ranks 60th among the most congested cities globally, with drivers spending an aver- age of 45 hours annually in traffic. Traditional methods for monitoring congestion, such as CCTV, are limited in coverage, leading to gaps in traffic data. Alternatively, crowdsourced data from Twitter, gathered via the Twitter API, offers an effective solution by providing real-time traffic updates, including reports on congestion, accidents, and road closures. This research leverages Twitter data to analyze congestion patterns and uses geovisualization through the Space-Time Cube (STC) method to present a comprehensive 3D representation of congestion. STC allows for the visualization of spatial and temporal patterns, enhancing the analysis of congestion dynamics. The study demonstrates that Twitter data provides accurate congestion information, validated through a t-test with a 95% confidence level. By combining crowdsourced data and STC, this approach offers a more dynamic, detailed, and informative method for understanding and analyzing traffic congestion, which can support traffic planning and decision-making in large urban areas.