*Article* **Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System**

**Hsun-Ping Hsieh 1,\*, Fandel Lin 2, Jiawei Jiang 1, Tzu-Ying Kuo 2 and Yu-En Chang 1**


**\***Correspondence: hphsieh@mail.ncku.edu.tw

**Abstract:** Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, 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 builtin 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 governmen<sup>t</sup> with 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 evaluate real-world data from New York City's bike-sharing system, and show that our LDA framework outperforms baseline approaches.

**Keywords:** bike sharing system; expansion areas; category clustering; batches prediction
