• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices
【24h】

Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices

机译：利用来自移动设备的惯性传感器数据在城市网络中构建车辆轨迹的算法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Vehicle trajectories are an important source of information for estimating traffic flow characteristics. Lately, several studies have focused on identifying a vehicle's trajectory in traffic network using data from mobile devices. However, these studies predominantly employed GPS coordinate information for tracking a vehicle's speed and position in the transportation network. Considering the known limitations of GPS, such as, connectivity issues at urban canyons and underpasses, low precision of localization, high power consumption of device while GPS is in use, this research focuses on developing alternate methods for identifying a vehicle's trajectory at an intersection and at a urban grid network using sensor data other than GPS in order to minimize GPS dependency. In particular, accelerometer and gyroscope data collected using smartphone's inertial sensors, and speed data collected using an on-board diagnostics (OBD) device, are utilized to develop algorithms for maneuver (i.e., left/right turn and through), trip direction, and trajectory identification.;Different algorithms using threshold of gyroscope and magnetometer readings, and machine learning techniques such as k-medoids clustering and dynamic time warping are developed for maneuver identification and their accuracy is tested on collected field data. It is found that, clustering based on maximum and minimum value of gyroscope readings is effective for maneuver identification. For trip direction identification at an intersection, two different methods are developed and tested. The first method utilizes accelerometer, gyroscope and OBD speed data, and the 2nd method employs magnetometer and acceleration data. The results demonstrate that the developed method using accelerometer, gyroscope and OBD speed data are effective in identifying a vehicle's direction. An effective algorithm is developed using OBD speed information, maneuver and trip direction identification algorithms to identify vehicle's trajectory at a grid network. Techniques for noise removal and orientation correction to transfer the raw data from phone's local coordinate to global coordinate system are also demonstrated.;Overall, this research eliminates the need for continuous GPS connectivity for trajectory identification. This research can be incorporated in methods developed by researchers to estimate traffic flow, delays, and queue lengths at intersections. This information can lead to better signal timings, travel recommendations, and traffic updates.
机译：车辆轨迹是估计交通流量特性的重要信息来源。最近，一些研究集中在使用来自移动设备的数据识别交通网络中的车辆轨迹。但是，这些研究主要使用GPS坐标信息来跟踪车辆在运输网络中的速度和位置。考虑到GPS的已知局限性，例如城市峡谷和地下通道的连通性问题，定位精度低，使用GPS时设备的功耗高等，本研究着重于开发识别交叉路口车辆轨迹的替代方法。在城市网格网络中使用GPS以外的其他传感器数据来最大程度地降低GPS依赖性。特别是，使用智能手机的惯性传感器收集的加速度计和陀螺仪数据以及使用车载诊断（OBD）设备收集的速度数据可用于开发机动（即，左转/右转和通过），行程方向和开发了使用陀螺仪和磁力计读数阈值的不同算法，以及针对k-medoids聚类和动态时间规整等机器学习技术来进行机动识别，并在收集的现场数据上测试了它们的准确性。发现，基于陀螺仪读数的最大值和最小值的聚类对于操纵识别是有效的。为了确定交叉路口的行进方向，开发并测试了两种不同的方法。第一种方法利用加速度计，陀螺仪和OBD速度数据，第二种方法利用磁力计和加速度数据。结果表明，使用加速度计，陀螺仪和OBD速度数据开发的方法可有效识别车辆的方向。使用OBD速度信息，操纵和行进方向识别算法开发了一种有效的算法，以在网格网络上识别车辆的轨迹。还展示了用于将原始数据从手机的本地坐标传输到全局坐标系的降噪和方向校正技术。总体而言，这项研究消除了为轨迹识别而连续使用GPS连接的需求。这项研究可以纳入研究人员开发的方法中，以估计交叉路口的交通流量，延误和排队长度。此信息可以导致更好的信号定时，出行建议和交通更新。

著录项

  • 作者

    Ahmed, Umama.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Transportation.;Computer science.;Engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 163 p.
  • 总页数 163
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号