Predicting real-time surge pricing of ride-sourcing companies
Predicting real-time surge pricing of ride-sourcing companies
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DOI：
10.1016/j.trc.2019.08.019
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发表时间：
2019-10-01
影响因子：
8.3
通讯作者：
Qian, Zhen (Sean)
中科院分区：
文献类型：
--
作者：
Battifarano, Matthew;Qian, Zhen (Sean)
Uber和Lyft等乘车外包公司代表了全球城市流行且不断增长的交通模式。这些公司在真实的时间内采用高峰定价，以平衡司机和乘客的需求。对未来几分钟至几小时内价格飙升的预测，概括了短期内服务车队和服务需求的复杂演变。如果能有效地预测并向司机和乘客传播高峰定价，可以更有效地分配车辆，节省用户的金钱和时间，并为司机提供有利可图的见解，最终有助于提高交通网络的效率和可靠性。本文探讨了城市环境、交通流特性和激增乘数之间的时空相关性。我们提出了一个通用的框架，用于预测浪涌乘数的短期演变的实时使用对数线性模型与L-1正则化，再加上模式聚类。该模型能够使用前一个小时的数据提前两小时预测匹兹堡的Uber激增乘数，在匹兹堡49个地点中，除了3个地点外，所有地点的总体平均值和历史平均值都优于其他所有地点，并且在49个地点中的28个地点优于三种非线性方法。该模型能够提前20分钟在匹兹堡的Lyft激增乘数上超越整体平均值，历史平均值和非线性方法。还探讨了Uber和Lyft激增乘数的交叉相关性。
Ride-sourcing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. The prediction of surge prices in the next few minutes to hours encapsulates the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the efficiency and reliability of transportation networks. This paper explores the spatio-temporal correlations between the urban environment, traffic flow characteristics, and surge multipliers. We propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a log-linear model with L-1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in Pittsburgh up to two hours in advance using data from the previous hour out-performing the overall mean and the historical average in all but 3 of the 49 locations in Pittsburgh and outperforming three nonlinear methods in 28 of the 49 locations. The model is able to out-perform the overall mean, historical mean, and non-linear methods on Lyft surge multipliers in Pittsburgh up to 20 min in advance. Cross-correlation of Uber and Lyft surge multipliers is also explored.