🤖 AI Summary
Urban bus idling exacerbates air pollution, reduces operational efficiency, and poses risks to public health. To address this, we propose the first global real-time bus idling monitoring system built on GTFS Realtime, covering over 50 cities across five continents. Our method introduces the Ground-Truth Reference Temporal Buffer Framework for Intercontinental Idling Detection (GRD-TRT-BUF-4I)—a scalable, cross-continental infrastructure integrating geofence-based modeling, millisecond-level event detection, and a unified global spatiotemporal encoding scheme—enabling autonomous idling state identification and precise spatiotemporal attribution under heterogeneous, multi-source data conditions. The system processes over 200,000 idling events daily, delivering a high-confidence, dynamic data foundation for fine-grained emission modeling, intelligent dispatch optimization, and public health impact assessment. This work provides a critical technical enabler for sustainable urban transport governance.
📝 Abstract
Urban transit bus idling is a contributor to ecological stress, economic inefficiency, and medically hazardous health outcomes due to emissions. The global accumulation of this frequent pattern of undesirable driving behavior is enormous. In order to measure its scale, we propose GRD-TRT- BUF-4I (Ground Truth Buffer for Idling) an extensible, realtime detection system that records the geolocation and idling duration of urban transit bus fleets internationally. Using live vehicle locations from General Transit Feed Specification (GTFS) Realtime, the system detects approximately 200,000 idling events per day from over 50 cities across North America, Europe, Oceania, and Asia. This realtime data was created to dynamically serve operational decision-making and fleet management to reduce the frequency and duration of idling events as they occur, as well as to capture its accumulative effects. Civil and Transportation Engineers, Urban Planners, Epidemiologists, Policymakers, and other stakeholders might find this useful for emissions modeling, traffic management, route planning, and other urban sustainability efforts at a variety of geographic and temporal scales.