Using machine learning for direct demand modeling of ridesourcing services in Chicago
Using machine learning for direct demand modeling of ridesourcing services in Chicago
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DOI：
10.1016/j.jtrangeo.2020.102661
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发表时间：
2020-02
影响因子：
6.1
通讯作者：
X. Yan;Xinyu Liu;Xilei Zhao
中科院分区：
文献类型：
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作者：
X. Yan;Xinyu Liu;Xilei Zhao
乘车外包服务的指数级增长一直在扰乱交通运输部门，并改变人们的出行方式。随着乘车服务的不断普及，能够准确预测其需求对于有效的土地利用和交通规划和政策制定至关重要。使用最近发布的行程级乘车服务数据在芝加哥沿着从公开的数据源获得的一系列变量，我们应用随机森林，广泛应用的机器学习技术，估计一个区域到区域（人口普查区）的乘车服务的直接需求模型。与传统的乘性模型相比，随机森林模型具有更好的模型拟合度和更高的预测精度。我们发现，社会经济和人口统计变量共同对随机森林模型的预测能力贡献最大（约50%）。出行阻抗、建筑环境特征和交通供给相关变量在出行需求预测中也是不可或缺的。
The exponential growth of ridesourcing services has been disrupting the transportation sector and changing how people travel. As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. Using recently released trip-level ridesourcing data in Chicago along with a range of variables obtained from publicly available data sources, we applied random forest, a widely-applied machine learning technique, to estimate a zone-to-zone (census tract) direct demand model for ridesourcing services. Compared to the traditional multiplicative models, the random forest model had a better model fit and achieved much higher predictive accuracy. We found that socioeconomic and demographic variables collectively contributed the most (about 50%) to the predictive power of the random forest model. Travel impedance, the built-environment characteristics, and the transit-supply-related variables are also indispensable in ridesourcing demand prediction.