  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. To this
end, this study explores various machine learning models and transfer learning
strategies for cross-modal demand prediction. The trip data of bike-share,
metro, and taxi are processed as the station-level passenger flows, and then
the proposed prediction method is tested in the large-scale case studies of
Nanjing and Chicago. The results suggest that prediction models with transfer
learning perform better than unimodal prediction models. Furthermore, stacked
Long Short-Term Memory model performs particularly well in cross-modal demand
prediction. These results verify our combined method's forecasting improvement
over existing benchmarks and demonstrate the good transferability for
cross-modal demand prediction in multiple cities.
