论文标题
考虑天气条件的影响，在自行车共享系统中进行建模
Modeling bike counts in a bike-sharing system considering the effect of weather conditions
论文作者
论文摘要
该论文开发了一种量化天气条件对旧金山湾区自行车共享系统中自行车站数量的影响的方法。随机森林技术用于对预测因子进行排名，然后使用引导的前进阶梯回归方法来开发回归模型。贝叶斯信息标准用于各种预测模型的开发和比较。我们证明，在具有大数据的大型网络的情况下，提出的方法有望量化各种特征对大型BSS和每个站点的影响。结果表明，当天，温度和湿度水平（以前尚未研究）是重要的计数预测因子。它还表明，由于天气变量是地理位置依赖性的，因此在使用建模之前应进行量化。此外，调查结果表明，T-1和当天时间I车站I的可用自行车数量是估计一站式自行车计数的最重要变量。
The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.
