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Forecasting Public Transport Ridership: Management of Information Systems using CNN and LSTM Architectures.
- Source :
- Procedia Computer Science; 2021, Vol. 184, p283-290, 8p
- Publication Year :
- 2021
-
Abstract
- This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 184
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 150336478
- Full Text :
- https://doi.org/10.1016/j.procs.2021.03.037