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Real time vehicle speed prediction using a Neural Network Traffic Model | IEEE Conference Publication | IEEE Xplore

Real time vehicle speed prediction using a Neural Network Traffic Model


Abstract:

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the indiv...Show More

Abstract:

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.
Date of Conference: 31 July 2011 - 05 August 2011
Date Added to IEEE Xplore: 03 October 2011
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Conference Location: San Jose, CA, USA

References

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