Machine learning til realtidsforudsigelser af oprindelse-til-destination efterspørgsel for jernbaner med smart card og udbudsdata : Udvidet resumé

Realtidsforudsigelser af passagerefterspørgsel på jernbanen kan bidrage til smartere trafikstyring og på sigt til at udvikle et offentligt transportsystem som på forskellig vis imødekommer ekstraordinær efterspørgsel. Dette kræver adgang til detaljeret information om efterspørgselsmønstre i form af løbende indsamling af passagertal for hvert par af oprindelses- og destinationsstationer i korte tidsintervaller. I dette studie udvikles en machine learning model til forudsigelser af afvigelser fra det periodiske efterspørgselsmønster på Københavns S-bane i 15 minutters intervaller ved hjælp af realtidsdata fra Rejsekortet på efterspørgselssiden og Banedanmarks driftsstatistikker på udbudssiden. Studiet belyser dels betydningen af udbud for forudsigelse af efterspørgsel og dels udforskes måden hvorpå spatiotemporal data indlejres i modeller fra dyb læring for at opnå nøjagtige forudsigelser for mange-dimensionale og sparsomme data som disse

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