2 research outputs found
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Short-term origin-destination (OD) flow prediction in urban rail transit
(URT) plays a crucial role in smart and real-time URT operation and management.
Different from other short-term traffic forecasting methods, the short-term OD
flow prediction possesses three unique characteristics: (1) data availability:
real-time OD flow is not available during the prediction; (2) data
dimensionality: the dimension of the OD flow is much higher than the
cardinality of transportation networks; (3) data sparsity: URT OD flow is
spatiotemporally sparse. There is a great need to develop novel OD flow
forecasting method that explicitly considers the unique characteristics of the
URT system. To this end, a channel-wise attentive split-convolutional neural
network (CAS-CNN) is proposed. The proposed model consists of many novel
components such as the channel-wise attention mechanism and split CNN. In
particular, an inflow/outflow-gated mechanism is innovatively introduced to
address the data availability issue. We further originally propose a masked
loss function to solve the data dimensionality and data sparsity issues. The
model interpretability is also discussed in detail. The CAS-CNN model is tested
on two large-scale real-world datasets from Beijing Subway, and it outperforms
the rest of benchmarking methods. The proposed model contributes to the
development of short-term OD flow prediction, and it also lays the foundations
of real-time URT operation and management.Comment: This paper has been accepted by the Transportation Research Part C:
Emerging Technologies as a regular pape