In this paper we describe an intelligent taxi dispatch system that has the goal of reducing the waiting time of the passengers and the idle driving distance of the taxis. The system relies on two separate models that predict the probability distributions of the taxi demand and destinations respectively. The models are learned from historical data and use a combination of long short term memory cells and mixture density networks. Using these predictors, taxi dispatch is formulated as a mixed integer programming problem. We validate the performance of the predictors and the overall system on a real world dataset of taxi trips in New York City.

中文翻译：

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基于需求和目的地预测的出租车调度系统

在本文中，我们描述了一种智能出租车调度系统，其目标是减少乘客的等待时间和出租车的空闲行驶距离。该系统依赖于两个独立的模型，分别预测出租车需求和目的地的概率分布。这些模型是从历史数据中学习的，并结合使用了长短期记忆单元和混合密度网络。使用这些预测器，出租车调度被表述为一个混合整数规划问题。我们在纽约市出租车行程的真实世界数据集上验证了预测器和整个系统的性能。