@article{ref1,
title="Comparative study on the prediction of city bus speed between LSTM and GRU",
journal="International journal of automotive technology",
year="2022",
author="Hwang, Giyeon and Hwang, Yeongha and Shin, Seunghyup and Park, Jihwan and Lee, Sangyul and Kim, Minjae",
volume="23",
number="4",
pages="983-992",
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.<p /> <p>Language: en</p>",
language="en",
issn="1229-9138",
doi="10.1007/s12239-022-0085-z",
url="http://dx.doi.org/10.1007/s12239-022-0085-z"
}