International Journal of Sustainable Transportation ( IF 3.963 ) Pub Date : 2020-10-05 , DOI: 10.1080/15568318.2020.1827316 Tao Wang 1 , Songhua Hu 2 , Yuan Jiang 3
Abstract
Flexible drop-off and pick-up (one-way) carsharing programs provide users with high levels of convenience but meanwhile incurs spatiotemporal imbalances in shared-cars distribution. Predicting shared-car use helps recognize system imbalances beforehand while identifying determinants related to shared-car use helps operators efficiently implement relocation strategies. In this study, a gradient boosting regression model (GBRT) is employed to predict shared-car use at a station level, and partial dependence plots (PDPs) are employed to examine nonlinear relationships between shared-car use and various predictors. Results show: (1) GBRTs predict shared-car use with a high level of accuracy (MSE: 1.1069–1.1648). (2) PDPs present highly consistent results with relationships derived from the traditional statistical model; (3) Time-varying variables account for 89.30%–86.84% importance in shared-cars use prediction, suggesting these variables can greatly enhance prediction accuracy; (4) Other variables like built environment, station attributes, and socioeconomic features, also account for some importance and can enhance prediction accuracy. Findings help carsharing operators accurately predict the station-level shared-car use and optimally identify the best locations for stations, and thus maintain the operational efficiency of carsharing programs.
中文翻译:
使用梯度提升回归树预测共享汽车的使用并检查非线性效应
摘要
灵活的上下车(单向)汽车共享计划为用户提供了高度的便利,但同时会导致共享汽车分布的时空失衡。预测共享汽车的使用有助于提前识别系统失衡,同时识别与共享汽车使用相关的决定因素有助于运营商有效地实施搬迁策略。在本研究中,使用梯度提升回归模型 (GBRT) 来预测车站级别的共享汽车使用情况,并使用部分依赖图 (PDP) 来检查共享汽车使用与各种预测变量之间的非线性关系。结果显示:(1)GBRT 以高精度预测共享汽车的使用(MSE:1.1069–1.1648)。(2) PDP 呈现出与源自传统统计模型的关系高度一致的结果;(3) 时变变量在共享汽车使用预测中占89.30%–86.84%的重要性,表明这些变量可以大大提高预测精度;(4) 其他变量,如建筑环境、站点属性和社会经济特征,也有一定的重要性,可以提高预测精度。调查结果帮助汽车共享运营商准确预测车站级共享汽车的使用情况,并优化确定车站的最佳位置,从而保持汽车共享项目的运营效率。