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Bus arrival time prediction using artificial neural network model

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2 Author(s)
Jeong, R. ; Texas Transp. Inst., College Station, TX, USA ; Rilett, L.R.

A major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The objectives of this research are to develop and apply a model to predict bus arrival time using automatic vehicle location (AVL) data. In this research, the travel time prediction model considered schedule adherence and dwell times. Actual AVL data from a bus route located in Houston, Texas was used as a test bed. A historical data based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed the historical data based model and the regression models in terms of prediction accuracy.

Published in:

Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on

Date of Conference:

3-6 Oct. 2004

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