DeepPF: A deep learning based architecture for metro passenger flow prediction

By Yang Liu, Zhiyuan Liu, Jia Ruo. Transportation Research Part C: Emerging Technologies, 2019

397 citations recorded by the source corpus.

Abstract This study aims to combine the modeling skills of deep learning and the domain knowledge in transportation into prediction of metro passenger flow. We present an end-to-end deep learning architecture, termed as Deep Passenger Flow (DeepPF), to forecast the metro inbound/outbound passenger flow. The architecture of the model is highly flexible and extendable; thus, enabling the integration and modeling of external environmental factors, temporal dependencies, spatial characteristics, and metro operational properties in short-term metro passenger flow prediction. Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of integrating multi-source data. Numerical experiments demonstrate that the proposed DeepPF model can be extended to general conditions to fit the diverse constraints that exist in the transportation domain.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/369eb781ee3e4c82af862c2718c18a500931253f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Nizar Bouguila
2. Saeed Rahmani
3. Asiye Baghbani
4. Zachary Patterson

In Transportation

1. Nizar Bouguila
2. Saeed Rahmani
3. Asiye Baghbani
4. Zachary Patterson

Citations in the corpus

• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “Among many applied methods, deep learning-based methods have drawn the special attention of researchers in this area [77], [78], [79], [80].”