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首页> 外文期刊>Transportation Research Part B: Methodological >Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning
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Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning

机译：流数据预测动脉旅行时间：流建模和机器学习的混合方法

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This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA. The hybrid model provides a distinct advantage over pure statistical or pure traffic theory models in that it is robust to noisy data (due to the large volumes of historical data) and it produces forecasts using traffic flow theory principles consistent with the physics of traffic. Validation of the model is performed in two different ways. First, a large scale test of the model is performed by splitting the data source into two sets, using the first to produce the estimates and the second to validate them. Second, an alternate validation approach is presented. It consists of a 3-day experiment in which GPS data was collected once per second from 20 drivers on four routes through San Francisco, allowing for precise calculation of actual travel times. The model is run by down-sampling the data and validated using the travel times from these 20 drivers. The results indicate that this approach is a significant step forward in estimating traffic states throughout the arterial network using a relatively small amount of real-time data. The estimates from our model are compared to those given by a data-driven baseline algorithm, for which we achieve a 16% improvement in terms of the root mean squared error of travel time estimates. The primary reason for success is the reliance on a flow model of traffic, which ensures that estimates are consistent with the physics of traffic.
机译：本文提出了一种混合建模框架，用于使用流式GPS探测数据估算和预测动脉交通状况。该模型基于行之有效的通过信号交叉口的交通流理论，并与机器学习框架结合使用，既可以学习道路的静态参数（例如自由流速度或交通信号参数），也可以进行估计和预测通过动脉网络的旅行时间。自2009年3月以来，该方法的机器学习部分使用了Mobile Millennium系统收集的大量历史数据，每分钟有500多辆探测车报告其在加利福尼亚州旧金山的位置。混合模型与纯统计模型或纯交通理论模型相比，具有明显的优势，因为它对嘈杂的数据具有鲁棒性（由于大量的历史数据），并且使用符合交通物理原理的交通流理论原理进行预测。模型的验证以两种不同的方式执行。首先，通过将数据源分为两组来进行模型的大规模测试，其中第一组用于生成估计，第二组用于验证估计。其次，提出了一种替代的验证方法。它由一个为期3天的实验组成，该实验每秒从穿越旧金山的4条路线上的20位驾驶员每秒收集一次GPS数据，从而可以精确计算实际旅行时间。该模型通过对数据进行下采样来运行，并使用这20个驱动程序的行驶时间进行验证。结果表明，该方法是在使用相对少量的实时数据估算整个动脉网络的流量状态方面迈出的重要一步。将我们模型的估算值与数据驱动的基线算法给出的估算值进行比较，为此，我们将行程时间估算的均方根误差提高了16％。成功的主要原因是对交通流模型的依赖，这可以确保估算值与交通的物理性质保持一致。

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