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.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15648
• Supplemental Notes:
  • This paper was sponsored by TRB committee AP050 Standing Committee on Bus Transit Systems.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Patel, Yatri
  • Firat, Connor
  • Childers, Tegan
  • Sartipi, Mina
• Conference:
  • Transportation Research Board 100th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2021-1-5 to 2021-1-29
• Date: 2021

Language

• English

Media Info

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

Subject/Index Terms

• TRT Terms: Bus routes; Data models; Machine learning; Mathematical prediction; Neural networks; Public transit; Ridership
• Geographic Terms: Chattanooga (Tennessee)
• Subject Areas: Data and Information Technology; Economics; Passenger Transportation; Public Transportation;

Filing Info

• Accession Number: 01763803
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-02877
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:57AM