TY - JOUR
T1 - Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method
AU - Zhou, Tuqiang
AU - Wu, Wanting
AU - Peng, Liqun
AU - Zhang, Mingyang
AU - Li, Zhixiong
AU - Xiong, Yubing
AU - Bai, Yuelong
N1 - Funding Information:
This research is jointly supported by National Nature Science Foundation of China (Grant No. 52062015 and 51708218 ) and Jiangxi Provincial Major Science and Technology Project-5G Research Project (Grant No. 20193ABC03A005 ), and Narodowego Centrum Nauki, Poland (No. 2020/37/K/ST8/02748).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/1
Y1 - 2022/1
N2 - Unreliable transit services can negatively impact transit ridership and discourage passengers from regularly choosing public transport. As the most important content of bus service reliability, accurate bus arrival prediction can improve travel efficiency for enabling a reliable and convenient transportation system. Accordingly, this paper proposes a novel deep learning method, i.e. variational mode decomposition long short-term memory (VMD-LSTM), for bus travel speed prediction in urban traffic networks using a forecast of bus arrival information on variable time horizons. The method uses the temporal and spatial patterns of the average bus speed series. The results show that the VMD-LSTM model outperforms other models on forecasting bus link speed series in future time intervals, whereas the artificial neural network model achieves the worst prediction. In conclusion, the VMD-LSTM method can detect irregular peaks of transit samples from a series of temporal or spatial variations and performs better on major and auxiliary corridors.
AB - Unreliable transit services can negatively impact transit ridership and discourage passengers from regularly choosing public transport. As the most important content of bus service reliability, accurate bus arrival prediction can improve travel efficiency for enabling a reliable and convenient transportation system. Accordingly, this paper proposes a novel deep learning method, i.e. variational mode decomposition long short-term memory (VMD-LSTM), for bus travel speed prediction in urban traffic networks using a forecast of bus arrival information on variable time horizons. The method uses the temporal and spatial patterns of the average bus speed series. The results show that the VMD-LSTM model outperforms other models on forecasting bus link speed series in future time intervals, whereas the artificial neural network model achieves the worst prediction. In conclusion, the VMD-LSTM method can detect irregular peaks of transit samples from a series of temporal or spatial variations and performs better on major and auxiliary corridors.
KW - Bus service reliability
KW - Deep learning
KW - Multi-time interval forecasting
KW - Time series analysis
KW - VMD-LSTM method
UR - http://www.scopus.com/inward/record.url?scp=85116673367&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2021.108090
DO - 10.1016/j.ress.2021.108090
M3 - Article
AN - SCOPUS:85116673367
VL - 217
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 108090
ER -