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Short-Term Prediction of Traffic Flow Status for Online Driver Information
| 题名： | Short-Term Prediction of Traffic Flow Status for Online Driver Information |
| 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##Real-time traffic information##Foreign technology##Short-term prediction models##Intelligent transport systems (ITS)## |
| 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. |
| 总页数： | 96 |
| 报告类型： | 科技报告 |
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