Abstract
Given the vehicle speed during actual driving, it is possible to apply an advanced energy management strategy for achieving better efficiency and less emission. We conducted a study to predict the future speed while driving of city buses, where only a few bus driving data and bus stop IDs are used without external complex traffic information. The speed prediction models were developed based on long time short memory (LSTM) and a gated recurrent unit (GRU), and a deep neural network (DNN) is also adopted for the bus stop ID processing. The performances of the models were analyzed and compared such that we found the LSTM-based model presents remarkable and practical prediction ability in accuracy and time spent. Adopting the proposed speed prediction model would make it a reality sooner, application of the optimal energy control strategy in the real world.
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- x t :
-
distance over time, m
- v t :
-
speed over time, m/s
- a t :
-
acceleration over time, m/s2
- σ :
-
sigmoid function
- tanh :
-
hyperbolic tangent function
- W 0 :
-
weight of neural network
- b 0 :
-
bias of neural network
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Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2C1090927 and No. 2021R1F1A1063048).
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Hwang, G., Hwang, Y., Shin, S. et al. Comparative Study on the Prediction of City Bus Speed Between LSTM and GRU. Int.J Automot. Technol. 23, 983–992 (2022). https://doi.org/10.1007/s12239-022-0085-z
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DOI: https://doi.org/10.1007/s12239-022-0085-z