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

References

• Ashqar, H. I., Elhenawy, M., Rakha, H. A., Almannaa, M., & House, L. (2022). Network and station-level bike-sharing system prediction: A San Francisco bay area case study. Journal of Intelligent Transportation Systems, 26(5), 602–612. https://doi.org/10.1080/15472450.2021.1948412
   Web of Science ®Google Scholar
• Babagoli, M. A., Kaufman, T. K., Noyes, P., & Sheffield, P. E. (2019). Exploring the health and spatial equity implications of the New York City Bike share system. Journal of Transport & Health, 13, 200–209. https://doi.org/10.1016/j.jth.2019.04.003
   PubMed Web of Science ®Google Scholar
• Chai, D., Wang, L., & Yang, Q. (2018). Bike flow prediction with multi-graph convolutional networks [Paper presentation]. 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 18), 397–400. https://doi.org/10.1145/3274895.3274896
   Google Scholar
• Chen, Q., Liu, M., & Liu, X. (2018). Bike fleet allocation models for repositioning in bike-sharing systems. IEEE Intelligent Transportation Systems Magazine, 10(1), 19–29. https://doi.org/10.1109/MITS.2017.2776129
   Web of Science ®Google Scholar
• Chen, J., Yang, Z., Cheng, P., & Shu, Y. (2021). Rebalancing bike-sharing system with deep sequential learning. IEEE Intelligent Transportation Systems Magazine, 13(4), 92–98. https://doi.org/10.1109/MITS.2019.2926252
   Web of Science ®Google Scholar
• Cho, J.-H., Ham, S. W., & Kim, D.-K. (2021). Enhancing the accuracy of peak hourly demand in bike-sharing systems using a graph convolutional network with public transit usage data. Transportation Research Record: Journal of the Transportation Research Board, 2675(10), 554–565. https://doi.org/10.1177/03611981211012003
   Web of Science ®Google Scholar
• Chu, J., Duan, Y., Yang, X., & Wang, L. (2020). The last mile matters: Impact of dockless bike sharing on subway housing price premium. Management Science, 67(1), 297–316. https://doi.org/10.1287/mnsc.2019.3550
   Web of Science ®Google Scholar
• Cleophas, C., Cottrill, C., Ehmke, J. F., & Tierney, K. (2019). Collaborative urban transportation: Recent advances in theory and practice. European Journal of Operational Research, 273(3), 801–816. https://doi.org/10.1016/j.ejor.2018.04.037
   Web of Science ®Google Scholar
• Coston, A., Ramamurthy, K. N., Wei, D., Varshney, K. R., Speakman, S., Mustahsan, Z., & Chakraborty, S. (2019). Fair transfer learning with missing protected attributes [Paper presentation]. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES 19), 91–98. https://doi.org/10.1145/3306618.3314236
   Google Scholar
• Deliali, A., Tainter, F., Ai, C., & Christofa, E. (2023). A framework for mode classification in multimodal environments using radar-based sensors. Journal of Intelligent Transportation Systems, 27(4), 441–458. https://doi.org/10.1080/15472450.2022.2051702
   Web of Science ®Google Scholar
• Fan, Y., & Zheng, S. (2020). Dockless bike sharing alleviates road congestion by complementing subway travel: Evidence from Beijing. Cities, 107, 102895. https://doi.org/10.1016/j.cities.2020.102895
   Web of Science ®Google Scholar
• Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. (2018). Transfer learning for time series classification [Paper presentation]. 2018 IEEE International Conference on Big Data (Big Data), 1367–1376. https://doi.org/10.1109/BigData.2018.8621990
   Google Scholar
• Guo, W., Fang, X., Jiang, L., Han, N., & Teng, S. (2023). Low-rank constraint-based multiple projections learning for cross-domain classification. Knowledge-Based Systems, 276, 110734. https://doi.org/10.1016/j.knosys.2023.110734
   Web of Science ®Google Scholar
• Hao, S., Lee, D.-H., & Zhao, D. (2019). Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transportation Research Part C: Emerging Technologies, 107, 287–300. https://doi.org/10.1016/j.trc.2019.08.005
   Web of Science ®Google Scholar
• Hua, M., Chen, J., Chen, X., Gan, Z., Wang, P., & Zhao, D. (2020). Forecasting usage and bike distribution of dockless bike-sharing using journey data. IET Intelligent Transport Systems, 14(12), 1647–1656. https://doi.org/10.1049/iet-its.2020.0305
   Web of Science ®Google Scholar
• Hua, M., Chen, X., Cheng, L., & Chen, J. (2021). Should bike-sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China. Journal of Transport & Health, 23, 101264. https://doi.org/10.1016/j.jth.2021.101264
   PubMed Web of Science ®Google Scholar
• Iacobucci, R., Donhauser, J., Schmöcker, J. D., & Pruckner, M. (2023). The demand potential of shared autonomous vehicles: A large-scale simulation using mobility survey data. Journal of Intelligent Transportation Systems. Advance online publication. https://doi.org/10.1080/15472450.2023.2205021
   Web of Science ®Google Scholar
• Lee, T., Sheng, L., Bozkaya, T., Balkir, N. H., Özsoyoglu, Z. M., & Özsoyoglu, G. (2002). Querying multimedia presentations based on content. In K. Jeffay & H. B. T.-R. N. Zhang (Eds.), The Morgan Kaufmann series in multimedia information and systems (pp. 413–437). Morgan Kaufmann. https://doi.org/10.1016/B978-155860651-7/50122-4
   Google Scholar
• Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2021). A multi-task memory network with knowledge adaptation for multimodal demand forecasting. Transportation Research Part C: Emerging Technologies, 131, 103352. https://doi.org/10.1016/j.trc.2021.103352
   Web of Science ®Google Scholar
• Li, A., Gao, K., Zhao, P., Qu, X., & Axhausen, K. W. (2021). High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data. Journal of Cleaner Production, 296, 126423. https://doi.org/10.1016/j.jclepro.2021.126423
   Web of Science ®Google Scholar
• Li, J., Guo, F., Sivakumar, A., Dong, Y., & Krishnan, R. (2021). Transferability improvement in short-term traffic prediction using stacked LSTM network. Transportation Research Part C: Emerging Technologies, 124, 102977. https://doi.org/10.1016/j.trc.2021.102977
   Web of Science ®Google Scholar
• Li, X., Liu, W., Qiao, J., Li, Y., & Hu, J. (2023). An enhanced semi-flexible transit service with introducing meeting points. Networks and Spatial Economics, 23(3), 487–527. https://doi.org/10.1007/S11067-022-09583-8/FIGURES/20
   Web of Science ®Google Scholar
• Li, X., Wang, T., Xu, W., & Hu, J. (2021). A novel model for designing a demand-responsive connector (DRC) transit system with consideration of users’ preferred time windows. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2442–2451. https://doi.org/10.1109/TITS.2020.3031060
   Web of Science ®Google Scholar
• Li, J., Xie, N., Zhang, K., Guo, F., Hu, S., & Chen, X. (2022). Network-scale traffic prediction via knowledge transfer and regional MFD analysis. Transportation Research Part C: Emerging Technologies, 141, 103719. https://doi.org/10.1016/j.trc.2022.103719
   Web of Science ®Google Scholar
• Li, Y., & Zheng, Y. (2020). Citywide bike usage prediction in a bike-sharing system. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1079–1091. https://doi.org/10.1109/TKDE.2019.2898831
   Web of Science ®Google Scholar
• Liang, Y., Huang, G., & Zhao, Z. (2021). Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach. https://doi.org/10.48550/arxiv.2112.08078
   Google Scholar
• Lin, L.,He, Z., &Peeta, S. (2018). Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transportation Research Part C: Emerging Technologies, 97, 258–276. https://doi.org/10.1016/j.trc.2018.10.011
   Web of Science ®Google Scholar
• Liu, Y., Liu, Z., & Jia, R. (2019). DeepPF: A deep learning based architecture for metro passenger flow prediction. Transportation Research Part C: Emerging Technologies, 101, 18–34. https://doi.org/10.1016/j.trc.2019.01.027
   Web of Science ®Google Scholar
• Liu, B., Liu, C., Xiao, Y., Liu, L., Li, W., & Chen, X. (2022). AdaBoost-based transfer learning method for positive and unlabelled learning problem. Knowledge-Based Systems, 241, 108162. https://doi.org/10.1016/j.knosys.2022.108162
   Web of Science ®Google Scholar
• Ma, J., Cheng, J. C. P., Jiang, F., Chen, W., Wang, M., & Zhai, C. (2020). A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. Energy and Buildings, 216, 109941. https://doi.org/10.1016/j.enbuild.2020.109941
   Web of Science ®Google Scholar
• Ma, X., Karimpour, A., & Wu, Y. J. (2024). Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows. Journal of Intelligent Transportation Systems. Advance online publication. https://doi.org/10.1080/15472450.2023.2301696
   Web of Science ®Google Scholar
• Ma, X., Zhang, J., Du, B., Ding, C., & Sun, L. (2019). Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2278–2288. https://doi.org/10.1109/TITS.2018.2867042
   Web of Science ®Google Scholar
• Nie, T.,Qin, G., &Sun, J. (2022). Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns. Transportation Research Part C: Emerging Technologies, 141, 103737. https://doi.org/10.1016/j.trc.2022.103737
   Web of Science ®Google Scholar
• Olivas, E. S., Guerrero, J. D. M., Martinez-Sober, M., Magdalena-Benedito, J. R., & López, A. J. S. (Eds.). (2009). Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques (Chapter 11, pp. 242–264). IGI Global. https://doi.org/10.4018/978-1-60566-766-9
   Google Scholar
• Pan, S., Trentesaux, D., Ballot, E., & Huang, G. Q. (2019). Horizontal collaborative transport: Survey of solutions and practical implementation issues. International Journal of Production Research, 57(15–16), 5340–5361. https://doi.org/10.1080/00207543.2019.1574040
   Web of Science ®Google Scholar
• Petersen, N. C., Rodrigues, F., & Pereira, F. C. (2019). Multi-output deep learning for bus arrival time predictions. Transportation Research Procedia, 41, 138–145. https://doi.org/10.1016/j.trpro.2019.09.025
   Google Scholar
• Toman, P., Zhang, J., Ravishanker, N., & Konduri, K. C. (2020). Dynamic predictive models for ridesourcing services in New York City using daily compositional data. Transportation Research Part C: Emerging Technologies, 121, 102833. https://doi.org/10.1016/j.trc.2020.102833
   Web of Science ®Google Scholar
• Wang, Q., Guo, B., Ouyang, Y., Cheng, L., Wang, L., Yu, Z., & Liu, H. (2022). Learning shared mobility-aware knowledge for multiple urban travel demands. IEEE Internet of Things Journal, 9(9), 7025–7035. https://doi.org/10.1109/JIOT.2021.3115174
   Web of Science ®Google Scholar
• Wang, B., Vu, H. L., Kim, I., & Cai, C. (2022). Short-term traffic flow prediction in bike-sharing networks. Journal of Intelligent Transportation Systems, 26(4), 461–475. https://doi.org/10.1080/15472450.2021.1904921
   Web of Science ®Google Scholar
• Wang, B., Yan, Z., Lu, J., Zhang, G., & Li, T. (2018). Road traffic flow prediction using deep transfer learning [Paper presentation]. Proceedings of the 13th International FLINS Conference (FLINS 2018), Vol. 11, 331–338. https://doi.org/10.1142/9789813273238_0044
   Google Scholar
• Wong, Y. Z., Hensher, D. A., & Mulley, C. (2020). Mobility as a service (MaaS): Charting a future context. Transportation Research Part A: Policy and Practice, 131, 5–19. https://doi.org/10.1016/j.tra.2019.09.030
   Web of Science ®Google Scholar
• Yang, Y., Heppenstall, A., Turner, A., & Comber, A. (2019). A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers, Environment and Urban Systems, 77, 101361. https://doi.org/10.1016/j.compenvurbsys.2019.101361
   Web of Science ®Google Scholar
• Yang, F., Yao, Z., Cheng, Y., Ran, B., & Yang, D. (2016). Multimode trip information detection using personal trajectory data. Journal of Intelligent Transportation Systems, 20(5), 449–460. https://doi.org/10.1080/15472450.2016.1151791
   Web of Science ®Google Scholar
• Yang, Y., Zhang, J., Yang, L., Yang, Y., Li, X., & Gao, Z. (2023). Short-term passenger flow prediction for multi-traffic modes: A transformer and residual network based multi-task learning method. Information Sciences, 642, 119144. https://doi.org/10.1016/j.ins.2023.119144
   Web of Science ®Google Scholar
• Zhang, J.,Chen, F.,Guo, Y., &Li, X. (2020). Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit. IET Intelligent Transport Systems, 14(10), 1210–1217. https://doi.org/10.1049/iet-its.2019.0873
   Web of Science ®Google Scholar
• Zhang, C., Zhang, H., Qiao, J., Yuan, D., & Zhang, M. (2019). Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE Journal on Selected Areas in Communications, 37(6), 1389–1401. https://doi.org/10.1109/JSAC.2019.2904363
   Web of Science ®Google Scholar
• Zhang, R., Isola, P., & Efros, A. A. (2017). Split-brain autoencoders: Unsupervised learning by cross-channel prediction [Paper presentation]. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1058–1067. https://doi.org/10.1109/CVPR.2017.76
   Google Scholar