近年來因為公共自行車共享系統的蓬勃發展，其相關研究被廣泛討論。在現有自行車站的基礎上部署更完善的系統對已開發國家抑或是發展中國家的政府而言都是至關重要的事情，因為這可能會高度影響日常通勤，甚至改變許多城市空間中的活動行為。現今許多研究多著重在準確地預期短時間內的單一站點需求量。然而，當考慮到自行車異常的使用情形、不定期的社會事件或交通壅塞的情況時，預測的任務便會變得困難許多。而相對來說，我們的目標在於給定拓展區域中自行車站點位置的前提下，預測自行車相對長期的租借需求量。在現實世界中，拓展區域中自行車站點的興建有批量興建的趨勢。為了解決該問題，我們提出了LDA（長期需求量指引）的框架系統來幫助估算新建站點的長期特徵。在LDA框架下，我們提出了幾種工程策略以提取具有識別性和代表性特徵來預測長期需求量。此外，針對原始站點和新建站點，我們提出數種特徵提取方法和演算法去對城市動態足跡和長期需求量之間的關聯性進行建模。我們的研究是第一個預測新建站點長期需求量的工作，它可以用來幫助政府提前評估部署新站點後的自行車流量，如此一來便可以避免浪費過多的資源，例如人事費用和預算。在通過紐約市自行車共享系統的真實數據的驗證後，說明我們所提出的LDA框架優於基線方法。

Research on flourishing public bike-sharing systems has been widely discussed in recent years. Whether it may be developed or developing countries, deploying a better bike system based on existing bike stations is a vital matter for the government, since it could affect everyday commute, and even change collective behaviors in urban spaces. Many existing works focus on accurately predicting individual stations in a short time. However, the task becomes difficult when it comes to abnormal bike usage patterns, irregular social events, or traffic jams. This work, however, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluated real-world data from New York City’s bike-sharing system, and it shows that our LDA framework outperforms baseline approaches.

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