Name
Title | ||
|---|---|---|
Inferring Activities and Optimal Trips: Lessons From Singapore's National Science Experiment. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-29643-2_19 | COMPLEX SYSTEMS DESIGN & MANAGEMENT ASIA: SMART NATIONS - SUSTAINING AND DESIGNING, CSD&M ASIA 2016 |
Keywords | DocType | Volume |
Urban data,Large-scale experiment,Sensor data,Optimal routing,Data visualization | Conference | 426 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Barnabé Monnot | 1 | 0 | 1.35 |
| Erik Wilhelm | 2 | 0 | 0.34 |
| Georgios Piliouras | 3 | 250 | 42.77 |
| Yuren Zhou | 4 | 0 | 0.68 |
| Daniel Dahlmeier | 5 | 460 | 29.67 |
| Hai Yun Lu | 6 | 0 | 0.34 |
| Wang Jin | 7 | 0 | 0.34 |