Version 1
: Received: 8 April 2021 / Approved: 9 April 2021 / Online: 9 April 2021 (15:04:06 CEST)
How to cite:
Osman, O.; Rakha, H.; Mittal, A. Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction. Preprints2021, 2021040269. https://doi.org/10.20944/preprints202104.0269.v1
Osman, O.; Rakha, H.; Mittal, A. Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction. Preprints 2021, 2021040269. https://doi.org/10.20944/preprints202104.0269.v1
Osman, O.; Rakha, H.; Mittal, A. Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction. Preprints2021, 2021040269. https://doi.org/10.20944/preprints202104.0269.v1
APA Style
Osman, O., Rakha, H., & Mittal, A. (2021). Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction. Preprints. https://doi.org/10.20944/preprints202104.0269.v1
Chicago/Turabian Style
Osman, O., Hesham Rakha and Archak Mittal. 2021 "Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction" Preprints. https://doi.org/10.20944/preprints202104.0269.v1
Abstract
This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.
Keywords
Travel Time Prediction; Deep Learning; Long Short Term Memory Networks; transit; temporal correlation
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
