Real-Time Urban Traffic Monitoring Using Transit Buses as Probes

Abstract

Real-time urban traffic monitoring is crucial for effective smart city management. Despite the increasing number of sensors collecting large-scale datasets in real time, challenges such as privacy concerns, high capital and maintenance costs, and limited coverage persist, impeding precise network traffic monitoring. General Transit Feed Specification (GTFS) Realtime data, an emerging real-time data source generated by public transit, exhibits high potential to monitor traffic given its public accessibility, low cost, and lack of privacy concerns. This study developed a new methodology leveraging GTFS Realtime data for citywide network sensing. Specifically, the proposed methodology uncovers the typical travel patterns of buses by isolating their operational events, involving boarding and alighting passengers at bus stops. Two algorithms, the segment-trip extraction algorithm and the segment speed estimation algorithm, were developed to implement the proposed methodology. The validation process used Bluetooth data collected in Gainesville, Florida, as the ground truth, while Google Traffic data served as a benchmark for comparison. Results indicate that the space mean speed estimated from GTFS Realtime data can better capture link speed trends and variations, similar to those observed in Bluetooth data. Furthermore, bus travel times derived from GTFS Realtime data demonstrated relatively high correlations with Bluetooth data and low prediction errors compared with estimates based on Google Traffic data. The proposed methodology and findings of this study can be directly used to complement and improve existing real-time traffic monitoring technologies.