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Abstract
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/create demand from/for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expected that cross-modal ripple effects will 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 transfer learning strategies and machine learning models 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. Fine-tuning without freezing strategy performs the best among all transfer learning strategies, and the split-brain strategy can handle the data missing problem. Furthermore, the 3-layer stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our deep transfer learning method’s forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities.
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.