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IPOD Abstract for presentation (Poster or Podium)

Data Sensing and Analytics

1667463 - Spatial Transferability of Machine Learning based Models for Ride-hailing Demand Prediction

Sunday, June 16, 2024
3:50 PM - 4:15 PM ET
Room: 219
Abstract:

Accurate prediction of ride-hailing demand is crucial to provide quality service to consumers, to schedule vehicles effectively, and to maintain a well-functioning transportation system. As information of ride-hailing demand in most of the cities is not available, assessing the spatial and temporal transferability of ride-hailing demand models is a critical research problem. To address this research problem, this study aims to develop a ride-hailing demand prediction model using trip information available from ride-hailing service providers and to test the spatial transferability of the model. Using aggregated trip data, we have developed ride-hailing generation and attraction prediction models using several well-known machine learning algorithms such as random forest, extreme gradient boost, support vector machine, and artificial neural network for two study areas including New York City and Chicago with similar built environment and land use characteristics. The developed models for New York City are later used to spatially transfer to predict the ride-hailing demand of Chicago using transfer learning approach. the spatial transfer mechanism evaluated in this experiment will provide valuable insights on potential transferability of the ride-hailing demand models.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • predict ride-hailing demands using aggregate trip information
  • evaluate the spatial transferability of the ride-hailing demand model
  • identify the key variables for ride-hailing demand prediction