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Intelligent city transportation systems are one of the core infrastructuresof a smart city. The true ingenuity of such an infrastructure lies in providingthe commuters with real-time information about citywide transports like publicbuses, allowing her to pre-plan the travel. However, providing priorinformation for transportation systems like public buses in real-time isinherently challenging because of the diverse nature of differentstay-locations that a public bus stops. Although straightforward factors stayduration, extracted from unimodal sources like GPS, at these locations lookerratic, a thorough analysis of public bus GPS trails for 720km of bus travelsat the city of Durgapur, a semi-urban city in India, reveals that several otherfine-grained contextual features can characterize these locations accurately.Accordingly, we develop BuStop, a system for extracting and characterizing thestay locations from multi-modal sensing using commuters' smartphones. Usingthis multi-modal information BuStop extracts a set of granular contextualfeatures that allow the system to differentiate among the differentstay-location types. A thorough analysis of BuStop using the collected datasetindicates that the system works with high accuracy in identifying differentstay locations like regular bus stops, random ad-hoc stops, stops due totraffic congestion stops at traffic signals, and stops at sharp turns.Additionally, we also develop a proof-of-concept setup on top of BuStop toanalyze the potential of the framework in predicting expected arrival time, acritical piece of information required to pre-plan travel, at any given busstop. Subsequent analysis of the PoC framework, through simulation over thetest dataset, shows that characterizing the stay-locations indeed helps makemore accurate arrival time predictions with deviations less than 60s from theground-truth arrival time.

Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction