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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions
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Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions

机译：基于视频的实时交通量测和可视化系统

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摘要

The ability to monitor the state of a given roadway in order to better manage traffic congestion has become increasingly important. Sophisticated traffic management systems able to process both the static and mobile sensor data and provide traffic information for the roadway network are under development. In addition to typical traffic data such as flow, density, and average traffic speed, there is now strong interest in environmental factors such as greenhouse gases, pollutant emissions, and fuel consumption. It is now possible to combine high-resolution real-time traffic data with instantaneous emission models to estimate these environmental measures in real time. In this paper, a system that estimates average traffic fuel economy, $hbox{CO}_{2}$ , CO, HC, and $hbox{NO}_{rm x}$ emissions using a computer-vision-based methodology in combination with vehicle-specific power-based energy and emission models is presented. The CalSentry system provides not only typical traffic measures but also gives individual vehicle trajectories (instantaneous dynamics) and recognizes vehicle categories, which are used in the emission models to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared with static emission inventory estimation models.
机译：监视给定道路状态以更好地管理交通拥堵的能力变得越来越重要。能够处理静态和移动传感器数据并为道路网络提供交通信息的复杂交通管理系统正在开发中。除了典型的交通数据（例如流量，密度和平均交通速度）外，现在还对诸如温室气体，污染物排放和燃料消耗等环境因素产生了浓厚的兴趣。现在可以将高分辨率的实时交通数据与瞬时排放模型结合起来，以实时估算这些环境措施。在本文中，该系统使用基于计算机视觉的方法估算了平均交通燃油经济性$ hbox {CO} _ {2} $，CO，HC和$ hbox {NO} _ {rm x} $排放量结合了特定于车辆的基于动力的能量和排放模型。 CalSentry系统不仅提供典型的交通措施，而且还给出了单个车辆的轨迹（瞬时动态）并识别了车辆类别，这些类别在排放模型中用于预测环境参数。与静态排放清单估算模型相比，该估算过程可提供更多动态和准确的环境信息。

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