| COMPARATIVE STUDY ON THE PREDICTION OF CITY BUS SPEED BETWEEN LSTM AND GRU
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| Giyeon Hwang 1, Yeongha Hwang 1, Seunghyup Shin 2, Jihwan Park 2, Sangyul Lee 3, Minjae Kim 1 |
1Department of Mechanical Engineering, Myongji University
2Department of Mechanical and Aerospace Engineering, Seoul National University
3Division of Mechanical and Electronics Engineering, Hansung University |
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| ABSTRACT |
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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. |
| Key Words:
Energy management strategy (EMS), Gated recurrent unit (GRU), Hybrid electric bus (HEB), Long shortterm memory (LSTM), Neural network, Speed predic
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Corresponding Author.
Minjae Kim , Email. 
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