Original scientific paper
https://doi.org/10.7307/ptt.v34i5.4052
Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach
Ruisen Jiang
; School of Transportation Engineering, Chang'an University
Dawei Hu
; School of Transportation Engineering, Chang'an University
Steven I-Jy Chien
; School of Transportation Engineering, Chang'an University
Qian Sun
; School of Transportation Engineering, Chang'an University
Xue Wu
; School of Transportation Engineering, Chang'an University
Abstract
The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective 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 Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance 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 combinations 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.
Keywords
bus travel time prediction; GPS data; electronic smart card data; long short-term memory model; genetic algorithm
Hrčak ID:
285198
URI
Publication date:
30.9.2022.
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