Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2019

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.

Keywords

Mobility Behavior, Regularity, Conformity, Live Smart City Services

Discipline

Social and Behavioral Sciences

Research Areas

Software and Cyber-Physical Systems

Publication

17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019; Seoul; South Korea; 2019 Jun 17-21

First Page

41

Last Page

53

ISBN

9781450366618

Identifier

10.1145/3307334.3326091

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3307334.3326091

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