自行車是近年來熱門的交通工具，尤其在城市中短距離的行程，共享自行車是最節能減碳的方案之一。世界各國知名的大城市幾乎都有提供共享自行車租賃系統，對於當地的居民或是外來的觀光客，這無疑是一大幫助，只要使用app 就可以查詢離自己較近的車站及車站狀態如：自行車數目與空車架數量等，使用者就可以前往租借或歸還自行車。然而，若附近的車站沒有可用的自行車或是空車架，使用者就必須花費較多的時間前往其他的車站。

有鑑於共享自行車系統的這種車輛與車架不足的問題，許多研究學者提出了解決方案，旨在讓使用者能夠不受時間和地點的限制使用自行車共享系統。本篇論文提出移動式車站的概念，有別於傳統共享自行車系統的車站是固定且無法移動的特性，移動式車站可以在使用者遇到租、還車問題時提供即時的幫助。舉例來說，當一個使用者來到車站準備還車，當下沒有空車架可供還車時，伺服器就可以派移動式車站過來支援，將該車站的部分自行車移上移動式車站，使得使用者能夠順利還車並接續之後的行程。

移動式車站及所需的服務機制已實作完成，並使用芝加哥的共享自行車系統Divvy 作為模擬環境。實驗結果顯示，移動式車站可以有效降低共享自行車系統遭遇車輛與車架不足的次數，讓使用者的租還車服務更為順利。

Bicycles have become an indispensable means of transportation in the city. In addition to saving money, the benefits of sharing bikes for the environment cannot be underestimated. The bike-sharing system is currently spread all over the world and most of the government agencies promote the use of public transportation. The primary challenge of the bike-sharing system is how to maintain the balance of the number of bikes and users’ demands at each station. Sometimes the bikes and empty docks at the station are not enough to meet the demands of users. Therefore, a bike-sharing system with mobile stations and fixed stations is proposed. A mobile station can linger between fixed stations. The mobile station can deliver bikes from fixed stations that have more bikes to those that are going to run out of bikes. The bike-sharing system with mobile stations has been implemented and evaluated successfully. The used data is provided by a bicycle sharing system, Divvy, in the city of Chicago. Experimental results show that mobile stations can effectively reduce the number of resource shortages in the bike-sharing system.

[1] Mountain Madness Tours. [Online]. Available: https://www.mountainmadnesstours.com/. [Accessed May, 2020].
[2] Chicago Reader. [Online]. Available: https://www.chicagoreader.com/chicago/divvy-bike-share-west-sideexpansion/Content?oid=22561087. [Accessed June, 2020].
[3] Divvy: Chicago’s Bike share Program | Divvy Bikes. [Online]. Available: https://www.divvybikes.com/. [Accessed May, 2020].
[4] Bike Share Map. [Online]. Available: https://bikesharemap.com/. [Accessed June 3, 2020]
[5] S. Ruffieux, E. Mugellini, and O. A. Khaled, “Bike Usage Forecasting for Optimal Rebalancing Operations in Bike-Sharing Systems,” IEEE International Conference on Tools with Artificial Intelligence, vol. 3, pp. 854–858, Nov. 2018.
[6] Y. Duan and J. Wu, “Optimizing the Crowdsourcing-based Bike Station Rebalancing Scheme,” IEEE International Conference on Distributed Computing Systems, vol. 15, pp. 1559–1568, July 2019.
[7] H. I. Ashqar, M. Elhenawy, M. H. Almannaa, A. Ghanem, H. A. Rakha, and L. House, “Modeling bike availability in a bike-sharing system using machine learning,” IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, pp. 374–378, June 2017.
[8] J. Zhang, P. Lu, Z. Li, and J. Gan, “Distributed Trip Selection Game for Public Bike System with Crowdsourcing,” IEEE International Conference on Computer Communications. (INFOCOM), p. 2717–2725, Apr. 2018.
[9] Q. Chen, M. Liu, and X. Liu, “Bike Fleet Allocation Models for Repositioning in Bike-Sharing Systems,” IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 1, p. 19–29, Spring 2018.
[10] General Bikeshare Feed Specification. [Online]. Available: https://nabsa.net/resources/gbfs/. [Accessed June 7, 2020]
[11] Chicago Data Portal. [Online]. Available: https://data.cityofchicago.org/. [Accessed June 7, 2020]
[12] Q. Chen, M. Liu, and X. Liu, “Mining Magnitude-Oblivious Periodical Patterns of Dockless Shared Bike Demands,” IEEE International Conference on Parallel and Distributed Systems, pp. 18–25, Dec. 2018.