Quantum mechanics

Function (biology)

Linguistics

Engineering

Philosophy

Biology

Data mining

Civil engineering

Computer science

Cardinality (data modeling)

Inflow

Evolutionary biology

Computer network

Data-driven

Physics

Urban rail transit

Mathematics

Flow (mathematics)

Feature (linguistics)

Artificial intelligence

Geometry

Channel (broadcasting)

Meteorology

Term (time)

Mechanics

Interpretability

Convolutional neural network

Machine learning

Outflow

Transportation Research Part C Emerging Technologies

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.

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method