基于GIS的城市轨道交通线网客流监测预警

在分析城轨客流特性的基础上,提出线网实时客流的监测算法;利用地理信息系统（GIS）技术,动态展示城市轨道交通线网实时客流分布态势;设定预警研判机理,对超过限定预警值的车站、区间的客流进行监测预警;借助GIS动态定位特性,辅助实现公交接驳的紧急疏散,以满足线网运营的可视化、高效的现代化管理。     

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。     

RS和GIS在高速公路采空区路段工程可行性研究中的应用

RS和GIS技术能全面宏观地分析工程区的地质背景及其对工程的影响,对降低工程成本及评价未来公路运行的风险具有重要意义。通过在某高速公路工程可行性研究中的应用,认为RS和GIS技术在高速公路穿越煤矿采空区等不良地质现象较发育地区的工程可行性研究之中,是一个十分有效的方法。     

GIS技术在公路建设管理中的应用研究

针对公路建设的发展以及工程项目管理发展的要求,介绍了道路CAD技术、地理信息系统(GIS)技术、数据库技术、网络技术在公路建设项目管理过程中的应用以及如何借助GIS的图形处理功能、信息表现及其空间分析功能对公路建设过程中的各工程实体的工程信息、计划进度、质量监理、费用管理等进行可视化表现和分析,规范和方便建设项目的科学管理,从而为公路建设的管理者和决策者提供直观、可靠、及时和有效的管理手段,便于作出相应的决策。     

基于GIS的区域道路交通安全管理系统研究

基于GIS的道路交通安全管理系统是将GIS、道路信息、交通事故信息及各类处置模型相集成的一个有机的整体。该整体利用GIS的可视性、空间分析能力和交通事故信息及相关资料的连续性、系统性通过相关的处置模型实现对系统所辖区域内各小区的道路安全水平的综合评价、预测,提出相应的区域道路网安全水平改善对策,从而达到提高整个区域道路网安全水平的目的。本文研究了基于GIS的道路安全管理系统的主要功能和对应于各功能的模块组成,提出了系统的建立程序。     

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.