BuSCOPE: Fusing Individual & Aggregated Mobility Behavior for “Live” Smart City Services

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BuSCOPE: Fusing Individual & Aggregated Mobility Behavior for “Live” Smart City Services
Title:
BuSCOPE: Fusing Individual & Aggregated Mobility Behavior for “Live” Smart City Services
Other Titles:
Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
Publication Date:
20 June 2019
Citation:
Abstract:
While analysis of urban commuting data has a long and demonstrated history of providing useful insights into human mobility behavior, such analysis has been performed largely in offline fashion and to aid medium-to-long term urban planning. In this work, we demonstrate the power of applying predictive analytics on real-time mobility data, specifically the smart-card generated trip data of millions of public bus commuters in Singapore, to create two novel and “live” smart city services. The key analytical novelty in our work lies in combining two aspects of urban mobility: (a) conformity: which reflects the predictability in the aggregated flow of commuters along bus routes, and (b) regularity: which captures the repeated trip patterns of each individual commuter. We demonstrate that the fusion of these two measures of behavior can be performed at city-scale using our BuScope platform, and can be used to create two innovative smart city applications. The Last-Mile Demand Generator provides O(mins) lookahead into the number of disembarking passengers at neighborhood bus stops; it achieves over 85% accuracy in predicting such disembarkations by an ingenious combination of individual-level regularity with aggregate-level conformity. By moving driverless vehicles proactively to match this predicted demand, we can reduce wait times for disembarking passengers by over 75%. Independently, the Neighborhood Event Detector uses outlier measures of currently operating buses to detect and spatiotemporally localize dynamic urban events, as much as 1.5 hours in advance, with a localization error of 450 meters.
License type:
PublisherCopyrights
Funding Info:
NRF2016NRFNSFC001-113
Description:
© ACM 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, https://doi.org/10.1145/3307334.3326091 .
ISBN:
978-1-4503-6661-8
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