Smart City Gnosys
Smart city article details
| Title | Exploiting Multi-Modal Contextual Sensing For City-Bus'S Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction |
|---|---|
| ID_Doc | 25416 |
| Authors | Mandal R.; Karmakar P.; Chatterjee S.; Das Spandan D.; Pradhan S.; Saha S.; Chakraborty S.; Nandi S. |
| Year | 2023 |
| Published | ACM Transactions on Internet of Things, 4, 1 |
| DOI | http://dx.doi.org/10.1145/3549548 |
| Abstract | Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters' smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time. © 2023 Association for Computing Machinery. |
| Author Keywords | Additional Key Words and PhrasesIntelligent transportation; machine learning; multi-modal sensing; smartphone computing; stay-location detection |
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