Real time vehicle speed prediction using a Neural Network Traffic Model
2011
Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.
Keywords:
- Floating car data
- Traffic flow
- Machine learning
- Traffic generation model
- InSync adaptive traffic control system
- Network traffic simulation
- Traffic congestion reconstruction with Kerner's three-phase theory
- Artificial intelligence
- Artificial neural network
- Control system
- Computer science
- Simulation
- Real-time computing
- Correction
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