Name
Abstract | ||
|---|---|---|
Current urban traffic congestion costs are increasing on account of the population growth of cities and increasing numbers of vehicles. Many cities are adopting intelligent transportation systems (ITSs) to improve traffic efficiency. ITSs can be used for monitoring traffic congestion using detectors, such as calculating an estimated time of arrival or suggesting a detour route. In this paper, we propose an urban traffic flow prediction system using a multifactor pattern recognition model, which combines Gaussian mixture model clustering with an artificial neural network. This system forecasts traffic flow by combining road geographical factors and environmental factors with traffic flow properties from ITS detectors. Experimental results demonstrate that the proposed model produces more reliable predictions compared with existing methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TITS.2015.2419614 | Intelligent Transportation Systems, IEEE Transactions |
Keywords | Field | DocType |
gaussian mixture model (gmm) clustering,intelligent transportation system (its),artificial neural network (ann),pattern recognition,traffic flow prediction,databases,predictive models,detectors,artificial neural networks | Traffic generation model,Traffic flow,Pattern recognition,Simulation,Estimated time of arrival,Artificial intelligence,Intelligent transportation system,Traffic congestion reconstruction with Kerner's three-phase theory,Engineering,Cluster analysis,Mixture model,Traffic congestion | Journal |
Volume | Issue | ISSN |
PP | 99 | 1524-9050 |
Citations | PageRank | References |
12 | 0.64 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Se-do Oh | 1 | 12 | 0.64 |
| Young-jin Kim | 2 | 21 | 1.51 |
| Ji-sun Hong | 3 | 12 | 0.64 |