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Ridango (Data Intelligence; LIT on-demand ticketing brand) — Consolidated bus/coach trip-level operational and passenger dataset: per-trip passenger loadings and connections, stop-level punctuality with trends, dwell times and inter-stop travel times, origin-stop departure events, real-time passenger volumes vs capacity, ticket-machine boarding records, telematics/AVL and GTFS-RT vehicle positions, incident records enriched with running board/block, mileage and passengers-impacted, CCTV-linked events, and driver-behaviour profiles (speeding, missed stops, late starts, unrecognised stops). Hig…
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Consolidated bus/coach trip-level operational and passenger dataset: per-trip passenger loadings and connections, stop-level punctuality with trends, dwell times and inter-stop travel times, origin-stop departure events, real-time passenger volumes vs capacity, ticket-machine boarding records, telematics/AVL and GTFS-RT vehicle positions, incident records enriched with running board/block, mileage and passengers-impacted, CCTV-linked events, and driver-behaviour profiles (speeding, missed stops, late starts, unrecognised stops)
From 10 yearsCoverage Information Technology · IndustrialsAsset class Equities
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Ridango (Data Intelligence; LIT on-demand ticketing brand) — Consolidated bus/coach trip-level operational and passenger dataset: per-trip passenger loadings and connections, stop-level punctuality with trends, dwell times and inter-stop travel times, origin-stop departure events, real-time passenger volumes vs capacity, ticket-machine boarding records, telematics/AVL and GTFS-RT vehicle positions, incident records enriched with running board/block, mileage and passengers-impacted, CCTV-linked events, and driver-behaviour profiles (speeding, missed stops, late starts, unrecognised stops). Hig…
Lit-transit offers (Alternative, Geospatial, Fundamental, Sentiment) — Dense and continuous: est. 10^7-10^8 stop-event records per operator per year (a 500-vehicle fleet x ~150 stops x 2 directions x ~300 service days ≈ 45M stop events/yr), each carrying arrival/departure timestamps, dwell and delay; passenger-loading records at tap-on/stop granularity on top; 1-5s telematics position pings during service (est. 10^8-10^9 pings/operator/yr); plus CCTV segments only around incident events, which keeps video volume bounded.
Weekly/periodic transit ridership panels at route-stop-time granularity; automated and computer-vision ridership counting ground truth (validation sets for CCTV/APC camera models, including the hard cases of door-obscuration and multi-door boarding); schedule integrity and timetable-reliability modeling; dwell-time and bus-lane ROI simulation; crowding-aware journey planning; driver-behaviour and safety scoring models; incident-impact and delay-propagation modeling; zero-fare evasion estimation from ticket-vs-boardings gaps; transfer-catch probability models for MaaS integration
The data is with 10 years of history.
Coverage spans US, Other, UK; Application Software, Passenger Ground Services; alternative, geospatial, fundamental, sentiment; equities.
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