论文标题
使用机器学习对自行车共享系统中的自行车可用性进行建模
Modeling bike availability in a bike-sharing system using machine learning
论文作者
论文摘要
本文使用机器学习算法在旧金山湾区自行车共享站的自行车可用性进行了建模。随机森林（RF）和最小二乘增强（LSBOOST）用作单变量回归算法，将部分最小二乘回归（PLSR）用作多变量回归算法。单变量模型用于对每个站点的可用自行车数进行建模。应用PLSR来减少所需的预测模型的数量，并反映网络中站点之间的空间相关性。结果清楚地表明，单变量模型比多元模型具有较低的误差预测。但是，多元模型结果对于具有较大空间相关站的网络是合理的。结果还表明，站邻居和预测范围时间是重要的预测指标。产生最小预测误差的最有效的预测范围时间是15分钟。
This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.
