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Inferring Activities and Optimal Trips: Lessons From Singapore's National Science Experiment
Conference proceeding

Inferring Activities and Optimal Trips: Lessons From Singapore's National Science Experiment

Barnabe Monnot, Erik Wilhelm, Georgios Piliouras, Yuren Zhou, Daniel Dahlmeier, Hai Yun Lu and Wang Jin
COMPLEX SYSTEMS DESIGN & MANAGEMENT ASIA: SMART NATIONS - SUSTAINING AND DESIGNING, CSD&M ASIA 2016, Vol.426, pp.247-264
Advances in Intelligent Systems and Computing
01/01/2016

Abstract

Computer Science Computer Science, Artificial Intelligence Computer Science, Theory & Methods Engineering Engineering, Electrical & Electronic Science & Technology Technology
The following paper presents three novel and efficient algorithms to tackle pressing questions asked by city planners as well as policy makers: Where are people starting and ending their trips? Which activities are people traveling to/from? Are they taking the most efficient route? In order to capture large-scale travel data, a novel sensor was developed by the Singapore University of Technology and Design together with industrial partners. Using computationally simple and scalable algorithms, we are able to understand the large amounts of data collected by the sensors and shed light on the three questions above.

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