TY  - JOUR
PY  - 2023//
TI  - Multi-step subway passenger flow prediction under large events using website data
JO  - Tehnicki Vjesnik
A1  - Tu, Qun
A1  - Geng, Guining
A1  - Zhang, Qianqian
SP  - 1585
EP  - 1593
VL  - 30
IS  - 5
N2  - An accurate and reliable forecasting method of the subway passenger flow provides the operators with more valuable reference to make decisions, especially in reducing energy consumption and controlling potential risks. However, due to the non-recurrence and inconsistency of large events (such as sports games, concerts or urban marathons), predicting passenger flow under large events has become a very challenging task. This paper proposes a method for extracting event-related information from websites and constructing a multi-step station-level passenger flow prediction model called DeepSPE (Deep Learning for Subway Passenger Flow Forecasting under Events). Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of website data in subway passenger flow forecasting under events.<p />  <p>Language: en</p>
LA  - en
SN  - 1330-3651
UR  - http://dx.doi.org/10.17559/TV-20230227000384
ID  - ref1
ER  -