SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation
SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation
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DOI：
10.1109/smartcomp.2017.7947048
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Chinmaya Samal;Fangzhou Sun;A. Dubey
中科院分区：
文献类型：
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作者：
Chinmaya Samal;Fangzhou Sun;A. Dubey
配备gps的探测车辆，特别是公共交通车辆产生的数据可以作为交通速度估计的可靠来源。传统上，这种估计是通过学习描述探测车辆的速度与实际交通速度之间关系的模型的参数来完成的。然而，这种方法通常会受到数据稀疏性问题的困扰。此外，大多数最先进的方法没有考虑天气和探测车辆驾驶员对学习模型参数的影响。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（包括探测车辆的实时位置、天气数据和匿名驾驶员标识符）中识别相似的操作集群，然后(b)使用这些不同的模型来实时估计交通速度作为当前天气、驾驶员和探测车辆速度的函数。当实时信息不可用时，我们的方法使用不同的模型，使用历史天气和交通信息进行估计。我们的研究结果表明，纯历史数据的准确性低于使用实时信息的模型。
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.