Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation
Summary :
Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.5 pp.1112-1115
- Publication Date
- 2022/05/01
- Publicized
- 2022/01/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDL8096
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Hao FANG
Fuzhou University
Chi-Hua CHEN
Fuzhou University
Dewang CHEN
Fujian University of Technology
Feng-Jang HWANG
University of Technology Sydney
Keyword
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Hao FANG, Chi-Hua CHEN, Dewang CHEN, Feng-Jang HWANG, "Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 1112-1115, May 2022, doi: 10.1587/transinf.2021EDL8096.
Abstract: Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8096/_p
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@ARTICLE{e105-d_5_1112,
author={Hao FANG, Chi-Hua CHEN, Dewang CHEN, Feng-Jang HWANG, },
journal={IEICE TRANSACTIONS on Information},
title={Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation},
year={2022},
volume={E105-D},
number={5},
pages={1112-1115},
abstract={Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.},
keywords={},
doi={10.1587/transinf.2021EDL8096},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 1112
EP - 1115
AU - Hao FANG
AU - Chi-Hua CHEN
AU - Dewang CHEN
AU - Feng-Jang HWANG
PY - 2022
DO - 10.1587/transinf.2021EDL8096
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.
ER -
English
