Department of Industrial Engineering, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain.
Sensors (Basel). 2022 Dec 28;23(1):312. doi: 10.3390/s23010312.
Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.
道路交通是城市中大部分空气污染物排放的主要来源，其排放浓度经常很高，超过了欧盟设定的限值。这对人类健康构成了严重威胁。在这方面，已经开发了建模方法来估算交通部门的排放因子。各国认为排放清单对于评估排放水平很重要，以便识别空气质量，并在这一领域进一步减少影响人类健康和环境的危险排放。这项工作的主要目标是设计和实现一个基于人工智能 (AI) 的系统，以估算真实道路的污染和消耗。该系统是一个由三个基本块组成的管道结构：分类和本地化、屏幕坐标到世界坐标转换和排放估算。作者提出了一个新的系统，将现有的技术（如卷积神经网络和排放模型）结合起来，使相机能够成为排放探测器。与其他真实世界的排放测量方法（激光雷达、速度和加速度传感器、天气传感器和摄像机）相比，我们的系统将所有测量集成到一个单一的传感器中：摄像机与处理单元相结合。该系统在地面真实数据集上进行了测试。我们的 AI 算法获得的速度估计与真实数据测量结果进行了比较，平均误差为 5.59%。然后，这些估计值被输入到一个模型中，以了解误差是如何传播的。这导致排放颗粒物的平均误差为 12.67%，排放气体的平均误差为 19.57%，消耗的燃料和能源的平均误差为 5.48%。
