Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach
The application of predicting bus travel time with re-al-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effec-tive to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genet-ic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model perfor-mance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various com-binations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.
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