Transfer learning for cross-modal demand prediction of bike-share and public transit

Understand

The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist.

• This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network.
• It is expectable that cross-modal ripple effects become more prevalent, with Mobility as a Service.
• Therefore, by propagating demand data across modes, a better demand prediction could be obtained.