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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Manasseh, C. Sengupta, R. |
| Copyright Year | 2013 |
| Description | Author affiliation: UC Berkeley, Berkeley, CA, USA (Sengupta, R.) || Human Intellect Lab., Falls Church, VA, USA (Manasseh, C.) |
| Abstract | In this paper we present a method for predicting the driver's destination with 96% accuracy. Knowing the driver's destination has many useful applications in traffic safety, traffic mobility, and influencing driver behavior. Furthermore, a software application that can predict the driver's destination can reduce the burden on the driver from manually entering the destination address on small-screen mobile devices. Current methods for predicting driver destination do that by predicting the driver's route. Those methods result in 72% accuracy if relying only on GPS traces. By providing accurate map data, the accuracy of prediction in current methods can reach 98% in best case scenarios that were tested on one subject. We propose an alternate approach that separates the destination prediction problem from the route prediction problem. We provide ten subjects, of varying driving patterns, with a Smartphone equipped with a GPS tracking software application and ask them to turn on the tracking application for all their trips for a period ranging between 2 and 9 weeks. We apply an algorithm to detect Origins and Destination from GPS traces and feed that into several machine learning algorithms to model the destination. Given the current position of the driver, the position at which the driver was 5 min prior, the time of day, and the day of the week; the algorithm provides a prediction of the destination with a 1000m resolution. The Decision Tree with Pruning algorithm proves to be the most accurate resulting in an average 96 +/- 1.72 % accuracy of prediction across all subjects. |
| Sponsorship | IEEE Intell.Transp. Syst. Soc. |
| Starting Page | 142 |
| Ending Page | 147 |
| File Size | 284273 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479929146 |
| DOI | 10.1109/ITSC.2013.6728224 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-06 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Global Positioning System Accuracy Decision trees Vehicles Software Machine learning algorithms Prediction algorithms |
| Content Type | Text |
| Resource Type | Article |
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