论文标题
短期预测乘车服务的需求：一种深度学习方法
Short term prediction of demand for ride hailing services: A deep learning approach
论文作者
论文摘要
随着乘车服务越来越流行，能够准确预测对此类服务的需求可以帮助操作员有效地将驾驶员分配给客户，并减少空闲时间，改善交通拥堵并增强乘客体验。本文提出了Ubernet，这是一个深度学习卷积神经网络，用于短期预测乘车服务的需求。 Ubernet Empploys一个多元框架，它利用文献中发现的许多时间和空间特征来解释对乘车服务的需求。提出的模型包括两个子网络，旨在编码各种特征的源序列并分别解码预测系列。为了评估Ubernet的性能和有效性，我们在2014年使用9个月的Uber拾取数据以及纽约市的28个时空功能。通过将Ubernet的性能与其他几种方法进行比较，我们表明该模型的预测质量具有很高的竞争力。此外，使用经济，社会和建筑环境特征时，Ubernet的预测性能更好。这表明Ubernet更自然地适合包括复杂的动机，以对乘车服务进行实时乘客需求预测。
As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. UberNet empploys a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. The proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet's prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.
