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Short-Term Prediction of Traffic Flow Status for Online Driver Information. Doctoral thesis.
| 题名： | Short-Term Prediction of Traffic Flow Status for Online Driver Information. Doctoral thesis. |
| 作者： | Innamaa-S. |
| 关键词： | *Travel-times; *Traffic-flow-status.;Online-models; Finland-; Information-accuracy; Prediction-models; Static-models; Dynamic-models; Monitoring-system-structure; Literature-reviews; Motor-vehicle-operators; Drivers-; Information-value. |
| 摘要： | The principal aim of this study was to develop a method for making a short-term prediction model of traffic flow status (i.e. travel time and a five-step travel-speed-based classification) and test its performance in the real world environment. Specifically, the objective was to find a method that can predict the traffic flow status on a satisfactory level, can be implemented without long delays and is practical for real-time use also in the long term. A sequence of studies shows the development process from offline models with perfect data to online models with field data. Models were based on MLP neural networks and self-organizing maps. The purpose of the online model was to produce real-time information of the traffic flow status that can be given to drivers. The models were tested in practice. |
| 报告类型： | 科技报告 |
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