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SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation

机译：SpeedPro：一种用于城市交通速度估计的预测性多模型方法

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摘要

Data generated by GPS-equipped probe vehicles, especially public transit vehicles can be a reliable source for traffic speed estimation. Traditionally, this estimation is done by learning the parameters of a model that describes the relationship between the speed of the probe vehicle and the actual traffic speed. However, such approaches typically suffer from data sparsity issues. Furthermore, most state of the art approaches does not consider the effect of weather and the driver of the probe vehicle on the parameters of the learned model. In this paper, we describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.
机译：配备GPS的探测车，尤其是公交车所产生的数据可以成为行车速度估算的可靠来源。传统上，这种估计是通过学习描述探查车速度与实际交通速度之间关系的模型参数来完成的。但是，此类方法通常会遇到数据稀疏性问题。此外，大多数现有技术方法没有考虑天气和探测车辆的驾驶员对学习模型的参数的影响。在本文中，我们描述了一种称为SpeedPro的多变量预测多模型方法，该方法（a）首先从历史数据中识别相似的操作簇，这些历史数据包括探测车的实时位置，天气数据和匿名驾驶员标识符，然后（b）使用这些不同的模型，根据当前天气，驾驶员和探测车的速度实时估算交通速度。当实时信息不可用时，我们的方法将使用不同的模型，该模型使用历史天气和交通信息进行估算。我们的结果表明，纯粹的历史数据不如使用实时信息的模型准确。

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