汽车天线偏心测试的系统设计与试验验证

随着智能网联汽车的迅速发展,汽车对外通信性能的重要性显著提高,但汽车体积大、重量沉,无法直接借鉴传统通信行业的天线系统测试方法。本文以智能网联汽车的天线系统为研究对象,研究出一种改进的近远场变换算法,并基于该方法设计出一种合理的验证方案,从而降低因汽车测试位置偏移对试验结果的影响。研究表明,当以整车作为被测件时,车载天线在测试系统中心和整车几何中心在测试系统中心的情况下,近场测试数据存在明显的较大差异;然而通过正确的NF-FF变换处理,两者远场辐射方向图都是一致的。说明了在球面近场系统测量加近远场变换的处理下,可以得到整车远场辐射信息。该结论对推动整车无线性能测试提供了实验和验证基础。     

城市轨道交通CBTC中准无缝切换的研究

在城市轨道交通中,当列车运行跨越由多个AP构成的大型无线网络时,存在切换的问题,切换过程中不可避免引起时延和丢包率问题。安全性是轨道交通永恒的主题,过长的时延和重要数据丢失都会引起行车安全。提出了基于位置的切换算法,减少切换时延的同时实现在相邻AP点间接力数据传输,消除丢包率,实现准无缝切换。     

现代有轨电车智能控制系统中的车辆定位技术方案

从现代有轨电车实际需求出发,参照沈阳市浑南新区现代有轨电车一期工程设计及建设经验,着重介绍现代有轨电车信号系统中的正线道岔控制系统、运营调度辅助系统等设计方案,可为后期国内及国际有轨电车信号系统设计提供借鉴。     

现代物流位置服务系统的设计

介绍了一个用于现代物流业的位置服务系统LTLBS，并着重就系统设计中的关键技术和与现代物流业有关的问题进行了分析。针对现代物流的特殊应用，在LTLBS中设计了一些特殊的功能，如货物跟踪，到达时间估计等。系统的性能分析表明定位精度和数据传输时延完全满足要求。     

理想条件下路网临界车头间距的研究

基于经典的跟驰理论，建立理想条件下车辆稳定跟驰行驶时的车头间距模型。并且利用高精度的车载GPS系统在北京市路网上采集的数据对该模型进行标定。同时根据最优化的相关理论确定出北京市路网达到其理想容量时车辆的临界行驶速度及临界车头间距，从而为确定理想路网容量奠定基础。     

汽车无线电导向行驶系统

介绍GPS系统概况、GPS全球卫星定位系统的基本原理及固定电台汽车导向行驶系统。     

基于WebGIS车辆定位系统的设计

探讨了如何利用WebGIS技术实现在Internet环境下车辆定位中的应用,包括系统的组成、工作原理、短消息和WebGIS架构等内容,该设计方法是在微软最新的开发环境.Net和Mapinfo公司的MapX控件基础上开发实现的,详细解释了WebGIS架构和介绍了车辆定位系统的主要功能。系统的实现使用户可以直接在Internet环境下完成查询指定车辆的位置等相关信息,为智能交通的实现奠定了基础。     

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.     

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.     

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.