Understanding real-world brake activity: A key to assessing non-tailpipe emission sources for sustainable transportation and communities

Electrification is considered a promising solution to environmental sustainability, due to the removal of tailpipe emissions (during operation) from the transportation sector. However, this would not have too much effect on those non-tailpipe particulate emissions. In addition, brake and tire wear particles are composed of various metals, rubber compounds, and organics which includes adhesives and have potential higher risks on the community health effects. In this study, the research team proposes to: 1) measure real-world brake activities of a large volume of vehicles traversing major roadway segments (e.g., near signalized intersections) by leveraging advanced roadside sensing technologies, e.g., Light Detection And Ranging and/or high-definition camera, as well as deep learning-based computer vision algorithms; and 2) construct the real-world brake activity database and supplement for the non-tailpipe emissions inventory. It is expected t..., , , # Data from: Understanding real-world brake activity: A key to assessing non-tailpipe emission sources for sustainable transportation and communities [https://doi.org/10.5061/dryad.41ns1rnrp](https://doi.org/10.5061/dryad.41ns1rnrp) ## Description of the data and file structure This dataset compiles multi-sensor data collected to analyze vehicle braking events and associated non-tailpipe particulate matter emissions at urban intersections. Data sources include GPS logs, LiDAR point clouds, video recordings, and onboard vehicle sensors. The dataset supports research on vehicle dynamics, braking behavior analysis, trajectory refinement, and emission estimation. ### Files and variables #### File: tableview\_250127\_050942.csv **Description:**Â ##### Variables * Index:Â Sequential index of the data points. * Lat (degrees):Â Latitude of the vehicle's location. * Lon (degrees):Â Longitude of the vehicle's location. * CoG (degrees):Â Course over ground (direction of movement). * SoG (m/s):...,电动化被视为实现环境可持续性的极具前景的解决方案，因其可消除交通运输行业运行过程中的尾气排放。然而，该方案对非尾气颗粒物排放的改善效果有限。此外，刹车片与轮胎磨损产生的颗粒物由多种金属、橡胶复合物及包括粘合剂在内的有机物组成，对公众健康存在潜在更高风险。本研究中，研究团队拟开展两项工作：1）借助先进的路边传感技术（如激光雷达（Light Detection And Ranging）和/或高清摄像机）以及基于深度学习的计算机视觉（Computer Vision）算法，对途经主要道路路段（如信号交叉口附近）的大量车辆进行实车制动行为监测；2）构建实车制动行为数据库，并以此补充非尾气排放清单。本研究预期…… ## 数据来源：Understanding real-world brake activity: A key to assessing non-tailpipe emission sources for sustainable transportation and communities [https://doi.org/10.5061/dryad.41ns1rnrp](https://doi.org/10.5061/dryad.41ns1rnrp) ## 数据与文件结构说明 本数据集整合了多传感器采集的数据，用于分析城市交叉口处的车辆制动事件及相关非尾气颗粒物排放。数据来源包括全球定位系统（GPS）日志、激光雷达（LiDAR）点云数据、视频录像以及车载传感器数据。本数据集可支撑车辆动力学、制动行为分析、轨迹精细化及排放估算等相关研究。 ### 文件与变量 #### 文件：tableview_250127_050942.csv **描述：** ##### 变量 * Index：数据点的顺序索引 * Lat（度）：车辆位置的纬度 * Lon（度）：车辆位置的经度 * CoG（度）：地面航向（车辆运动方向） * SoG（m/s）：……