Name
Abstract | ||
|---|---|---|
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-39653-6_6 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
bayesian model,expectation maximization | Mobile computing,Importance sampling,Bayesian inference,Simulation,Expectation–maximization algorithm,Computer science,Particle filter,Real-time computing,Global Positioning System,Assisted GPS,Level sensor,Distributed computing | Conference |
Volume | ISSN | Citations |
2864 | 0302-9743 | 251 |
PageRank | References | Authors |
53.71 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donald J. Patterson | 1 | 1765 | 219.99 |
| Lin Liao | 2 | 1649 | 209.71 |
| Dieter Fox | 3 | 12306 | 1289.74 |
| Henry A. Kautz | 4 | 9271 | 1010.27 |