基于扩展卡尔曼滤波的GPS信号跟踪技术

提出一种基于扩展卡尔曼滤波的GPS信号跟踪方法,通过扩展卡尔曼滤波器,得到基于相干积分支路的滤波模型,有效地削弱常规GPS跟踪环路中的跟踪误差,增强接收机的抗干扰性能,提升其在信号较弱位置下环路的跟踪性能,对加入惯性信息条件下惯性信息对系统所产生的影响做了相应的分析研究。通过仿真对比结果可以知道:当处于弱信号条件时,与通常用到的GPS信号跟踪方式相比,基于扩展卡尔曼滤波的信号跟踪算法可以有效提升跟踪的精度。     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

GPS天线线型对AUV阻力和尾流场的影响

靠近艉部的GPS天线线型对AUV的阻力以及桨盘面处的伴流品质有直接影响。高品质的伴流场能有效地降低噪声并提高水动力性能。为了获得最优的GPS天线线型,以某自主式水下机器人(AUV)为母型,采用ICEM-CFD、Fluent等CFD软件,结合RANS方法和SSTκ-ω模型对搭载不同线型GPS天线的AUV进行数值计算。研究结果表明GPS天线设计前倾角可以有效降低天线的阻力,从而降低AUV受到的整体阻力。具有一定前倾角和尾端曲线的GPS天线有利于改善AUV尾部伴流特性,减小桨盘面伴流速度的不均匀度。     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

基于特征子序列修正的公交刷卡滞后时间推算

针对一票制公共交通IC卡刷卡系统存在的滞后时间问题,提出基于特征子序列修正的时差匹配推算方法。依托GPS车辆到站信息序列和刷卡聚类序列,分析首末站和中途站站间运行时间分布的差异化特征,分别从两序列中提取大时间差和连续小时间差等特征子序列;利用两种子序列间一对一或一对多的对应关系分别进行初步搜索和二次修正,兼顾匹配过程的效率和精度。将推算流程应用于山西省大同市公共汽车系统,结果显示,抽样车辆的刷卡匹配率均达到99%以上,并且针对不同类型的公共汽车线路具有较强的适应性。     

Predicting market potential and environmental benefits of deploying electric taxis in Nanjing,China

This paper investigates the market potential and environmental benefits of replacing internal combustion engine (ICE) vehicles with battery electric vehicles (BEVs) in the taxi fleet in Nanjing, China. Vehicle trajectory data collected by onboard global positioning system (GPS) units are used to study the travel patterns of taxis. The impacts of charger power, charging infrastructure coverage, and taxi apps on the feasibility of electric taxis are quantified, considering taxi drivers’ recharging behavior and operating activities. It is found that (1) depending on the charger power and coverage, 19% (with AC Level 2 chargers and 20% charger network coverage) to 56% (with DC chargers and 100% charger network coverage) of the ICE vehicles can be replaced by electric taxis without driving pattern changes; (2) by using taxi apps to find nearby passengers and charging stations, drivers could utilize the empty cruising time to charge the battery, which may increase the acceptance of BEVs by up to 82.6% compared to the scenario without taxi apps; and (3) tailpipe emissions in urban areas could be significantly reduced with taxi electrification: a mixed taxi fleet with 46% compressed-natural-gas-powered (CNG) and 54% electricity-powered vehicles can reduce the tailpipe emissions by 48% in comparison with the fleet of 100% CNG taxis.     

基于GPS载波相位信号确定运动体姿态的关键技术

应用GPS载波相位信号确定运动体姿态,针对二维两天线系统情况,设计了姿态测量系统的硬件结构及算法流程框图,文章介绍了GPS姿态测量的关键技术：GPS姿态观测方程、整周模糊度的解算、误差的消除。从系统设计角度证明了技术的可行性。     

城市公交GPS轨迹路线图制作研究

以哈尔滨市104路公交为主要研究对象,详细介绍城市公交的GPS轨迹路线图制作时所需的软硬件,以及数据的采集方法,叙述数据的整合与分类过程。通过将主要公交站点周围兴趣点信息增添到轨迹点附近,构成包含有站点轨迹信息、站点影像信息的沿途轨迹信息。经过整合与分类,通过HOLUX ezTour软件对数据进行处理,阐述轨迹查询、地标的新增与删改和轨迹的添加与变换等功能。并使用Google Earth三维图像对轨迹路线全面整合,最终制作成城市公交GPS轨迹路线图。     

公路工程测量探讨

主要就公路测量的特点及过程进行介绍，论述公路测量中所采用的方法，解释公路测量在工程进行各个阶段的具体任务。另外介绍了GPS 技术在公路测量的应用前景。     

Using SenseCam to pursue “ground truth” for global positioning system travel surveys

Global Positioning System (GPS) surveys have been conducted for the past decade. Although GPS records were initially regarded as providing “ground truth” of travel, it has been found subsequently that they have some errors. SenseCam, a small passive digital camera, provides a chance to pursue ground truth by capturing images every 20 s on average. This paper discusses how SenseCam could help GPS data collection and shows potential benefits for both SenseCam and GPS research. This paper also investigates the performance of GPS devices in detail in terms of recording data by comparisons between GPS results and SenseCam images. The specific issue of missing GPS data is discussed and examined in this paper.