The Relation of Crowdsourced Data, Traffic Congestion Patterns, and Road Network in Samarinda City, Indonesia

This study analyzes traffic congestion in Samarinda City using a crowdsourcing approach based on real-time Google Maps data to identify congestion patterns and contributing factors. The research examines the relationship between road network characteristics, including road hierarchy, road width, intersections, and traffic density, across different urban road segments using a descriptive and quantitative approach. Secondary data were collected via the Google Maps Application Programming Interface (API) and processed in ArcGIS to produce congestion hotspot maps using the Getis-Ord Gi* spatial analysis method. The results indicate that between 11 and 17 August 2025, the highest congestion occurred on the Samarinda-Bontang Road. Intersections were identified as the most significant variable influencing congestion levels. Based on these findings, recommendations were made to improve the road network and manage intersections to reduce congestion in Samarinda City. This study also to urban planning by demonstrating the potential of crowdsourced data as an efficient tool for data collection and analysis to support sustainable transportation policies.