Ridership Prediction of New Bus Routes at Stop Level by Modelling Socio-economic Data using Supervised Machine Learning Methods

Predictive modeling is key to studying passengers' behavior in transportation research. Modelling the public transport system can be used to estimate present and future demand and users’ trend toward public transport services. Machine learning techniques have proven to be better at recognizing the patterns and relations in the data. While, the traditional techniques are aimed at forming casual relationships and are unable to recognize patterns in the data. This paper seeks to predict the ridership at stop level for the new bus routes using the socio-economic data, building data, and ridership data of the existing routes at stop level. Neural networks (NN), a machine learning method has been applied to build predictive models. Ridership of the existing routes has been used to train and validate the model performance, which is able to predict the public transport ridership of the new routes. This model can be used by public transport agencies and relevant government organizations to predict the public transport demand for new commuters before introducing any new changes in the public transit system.

Language

  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: 23p

Subject/Index Terms

Filing Info

  • Accession Number: 01763803
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02877
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:11AM