Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01032 | |
| Number of page(s) | 11 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801032 | |
| Published online | 08 September 2025 | |
- Bourne, J.E., Sauchelli, S., Perry, R., Page, A., Leary, S., England, C., and Cooper, A.R.: 'Health benefits of electrically-assisted cycling: a systematic review', Int. J. Behav. Nutr. Phys. Act., 2018, 15, pp. 1–15. [Google Scholar]
- Tarroja, B., Forrest, K., Yamada, K., Saha, R., and Hyland, M.: 'Estimating the Electricity System Benefits of Scaling up E-Bike Usage in California', J. Clean. Prod., 2025, (144840). [Google Scholar]
- Lou, L., Li, L., Yang, S.B., and Koh, J.: 'Promoting user participation of shared mobility in the sharing economy: Evidence from Chinese bike sharing services', Sustainability, 2021, 13, (3), pp. 1533. [CrossRef] [Google Scholar]
- Fu, C., Zhu, N., Pinedo, M., and Ma, S.: 'Station-based, free-float, or hybrid: An operating mode analysis of a bike-sharing system', Transp. Res. Part B Methodol., 2025, 191, pp. 103105. [Google Scholar]
- Luo, H., Kou, Z., Zhao, F., and Cai, H.: 'Comparative life cycle assessment of station-based and dock-less bike sharing systems', Resour. Conserv. Recycl., 2019, 146, pp. 180–189. [Google Scholar]
- Hua, M., Yu, X., Chen, X., Chen, J., and Cheng, L.: 'Can bike sharing achieve self-balancing distribution? Evidence from dockless and station-based cases', Travel Behav. Soc., 2025, 38, pp. 100879. [Google Scholar]
- Angelelli, E., Chiari, M., Mor, A., and Speranza, M.G.: 'A simulation framework for a station-based bike-sharing system', Comput. Ind. Eng., 2022, 171, pp. 108489. [Google Scholar]
- Jara-Díaz, S., Latournerie, A., Tirachini, A., and Quitral, F.: 'Optimal pricing and design of station-based bike-sharing systems: A microeconomic model', Econ. Transp., 2022, 31, pp. 100273. [Google Scholar]
- Silva, N., Werneck, H., Silva, T., Pereira, A.C., and Rocha, L.: 'Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions', Expert Syst. Appl., 2022, 197, pp. 116669. [CrossRef] [Google Scholar]
- Srikanth, A., Gowthaam, G., Gayathri, M., Goutham, D.T., Lakshana, C.R., and Lavaniya, H.: 'Dynamic personalized ads recommendation system using contextual bandits'. Proc. Int. Conf. Intelligent Systems for Communication, IoT and Security (ICISCoIS), Feb. 2023, pp. 339–344. [Google Scholar]
- Huo, X., and Fu, F.: 'Risk-aware multi-armed bandit problem with application to portfolio selection', R. Soc. Open Sci., 2017, 4, (11), pp. 171377. [Google Scholar]
- Şahin, Ü., Yücesoy, V., Koç, A., and TEKin, C.: 'Risk-averse ambulance redeployment via multi-armed bandits'. Proc. 26th Signal Processing and Communications Applications Conf. (SIU), May 2018, pp. 1–4. [Google Scholar]
- Trovo, F., Paladino, S., Restelli, M., and Gatti, N.: 'Sliding-window thompson sampling for non-stationary settings', J. Artif. Intell. Res., 2020, 68, pp. 311–364 [Google Scholar]
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