Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit
IEEE Transactions on Intelligent Transportation Systems
; : 2023/09/01 00:00:00.000, 2023.
Article
in English
| Scopus | ID: covidwho-2237640
ABSTRACT
Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. IEEE
Analytical models; Data models; Deep learning; Estimation; LSTM; OD prediction; Predictive models; Rails; real-time prediction; Real-time systems; separation rate; urban rail transit; Disasters; Interactive computer systems; Light rail transit; Long short-term memory; Mean square error; Real time systems; Flow prediction; Origin destination; Origin-destination flows; Origin-destination prediction; Real - Time system; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
IEEE Transactions on Intelligent Transportation Systems
Year:
2023
Document Type:
Article
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