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

arXiv: https://arxiv.org/abs/2203.09279 · HTML: https://mlsanity.com/paper/2203.09279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 70 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction](https://mlsanity.com/paper/1611.09842/) (2016)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Bike Flow Prediction with Multi-Graph Convolutional Networks](https://mlsanity.com/paper/1807.10934/) (2018)
- [Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/1712.04997/) (2017)
- [Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds](https://mlsanity.com/paper/1706.07680/) (2017)
- [Graph Markov Network for Traffic Forecasting with Missing Data](https://mlsanity.com/paper/1912.05457/) (2019)
- [Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach](https://mlsanity.com/paper/2112.08078/) (2021)
- [Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method](https://mlsanity.com/paper/2203.00422/) (2022)
- [Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study](https://mlsanity.com/paper/2009.09367/) (2020)

## Built on by (most cited first, 2 of 2)

- [Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks](https://mlsanity.com/paper/2211.08903/) (2022)
- [Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting](https://mlsanity.com/paper/2608.28273/) (2026)

## Abstract (arXiv)

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.