Efficient Traffic Density Prediction in Road Networks Using Suffix Trees
| dc.contributor.author | Kriegel, Hans-Peter | |
| dc.contributor.author | Renz, Matthias | |
| dc.contributor.author | Schubert, Matthias | |
| dc.contributor.author | Züfle, Andreas | |
| dc.date.accessioned | 2018-01-08T09:15:59Z | |
| dc.date.available | 2018-01-08T09:15:59Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a statistical approach to predict the density on any edge in such a network at a future point of time. Our method combines long-term and short-term observations of a traffic network in order to predict traffic density for the near future. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11305 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Efficient Traffic Density Prediction in Road Networks Using Suffix Trees | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 240 | |
| gi.citation.startPage | 233 |