1,385 research outputs found
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
Metro origin-destination prediction is a crucial yet challenging time-series
analysis task in intelligent transportation systems, which aims to accurately
forecast two specific types of cross-station ridership, i.e.,
Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete
OD matrices of previous time intervals can not be obtained immediately in
online metro systems, and conventional methods only used limited information to
forecast the future OD and DO ridership separately. In this work, we proposed a
novel neural network module termed Heterogeneous Information Aggregation
Machine (HIAM), which fully exploits heterogeneous information of historical
data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices)
to jointly learn the evolutionary patterns of OD and DO ridership.
Specifically, an OD modeling branch estimates the potential destinations of
unfinished orders explicitly to complement the information of incomplete OD
matrices, while a DO modeling branch takes DO matrices as input to capture the
spatial-temporal distribution of DO ridership. Moreover, a Dual Information
Transformer is introduced to propagate the mutual information among OD features
and DO features for modeling the OD-DO causality and correlation. Based on the
proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD
and DO ridership simultaneously. Extensive experiments conducted on two
large-scale benchmarks demonstrate the effectiveness of our method for online
metro origin-destination prediction
Multi-Step Subway Passenger Flow Prediction under Large Events Using Website Data
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
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