# BuSCOPE : Fusing Individual & Aggregated Mobility Behavior for "Live" Smart City Services

## TL;DR

This paper introduces BuSCOPE, a real-time analytics platform that fuses individual and aggregate mobility data to enable predictive smart city services like demand forecasting and event detection, improving urban transit efficiency.

## Contribution

It presents a novel fusion of conformity and regularity measures in mobility data for real-time smart city applications, a significant advancement over traditional offline analysis.

## Key findings

- Over 85% accuracy in predicting disembarkation demand
- Reduced passenger wait times by over 75%
- Detected urban events up to 1.5 hours in advance

## 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.