基于GIS的船运液化气泄漏事故应急信息系统

为了有效地减少海运液化气泄漏事故导致了人身伤亡与环境污染,我们将地理信息系统GIS组件COM技术应用在海运液化气泄漏事故上,开发研究了海运液体气泄漏事故应急信息系统。文章重点介绍了系统的总体设计、系统所能实现的功能和系统的开发工具及其集成技术。希望能为国内外同行的研究提供参考。     

浅谈GIS技术在公路管理中的应用

GIS技术目前在许多领域获得广泛应用,尤其在公路管理中,它与传统的交通分析和处理技术紧密结合延伸出公路地理信息系统,该系统对公路规划、设计和养护管理等具有较强的应用价值。     

Polycentricity and transit service

This paper focuses on the two related issues of employment distribution and access to transit services. Using the 2001 census tract level economic activities and transit routes within the county, a number of analyses were performed to determine the location of major employment centers in Los Angeles County and how these localities may be understood within the context of a transit service operation in a polycentric metropolitan area. The identified economic subcenters contain one-third of the county employment and its firms, collectively. While these economic nodes are networked by the existing bus routes, the connection between employees and their place of work appears to be inadequate. This has created a less than optimal condition in many sections of the metropolitan area. This paper suggests methodologies for encountering this shortcoming.     

基于GIS的城市交通预测系统

探讨了基于GIS的城市交通预测系统的基本功能，叙述了系统结构的方案设计，对每一个功能模块进行了简单的说明。     

GIS的信息处理与网络研究

通过Internet下的Client／Server模式的研究，分析了对GIS信息处理模式，并探讨了GIS服务网络及相关问题。     

基于GIS的Logistic回归在区域滑坡空间预测中的应用

巫山新址西区作为试验区,选取地形地貌、岩性特征、地质构造等区域斜坡稳定性影响因子,以MAPGIS地理信息系统为技术支撑,将全区按10m×10m方格划分为14450个单元,每个格网单元作为一个样本,建立Logistic回归模型,进行区域滑坡空间预测。结果表明:以滑坡发生概率0.163为判据,试验区内发生滑坡地段的判对率为72.08%,不发生滑坡地段的判对率为81.44%。根据计算结果可以得到区域斜坡稳定性分区以及进一步的地质灾害风险评价。     

基于GIS技术的一种新型组合电器

作为电力系统一次设备的革新,HGIS产品大胆采用新技术,在思维上和设计上均有很多创新,使其在结构上具有紧凑、小型化、连接电缆少和占地小的特点,在性能上具有可靠性高、维护量少的特点。该开关的设计思路体现了未来一次电气设备发展的方向,具有广阔的发展前景。本文详细地介绍了HGIS的产品特点和性能优势。     

铁路线路三维设计研究与开发

铁路选线技术与数字地面模型技术的结合，促使铁路设计向三维设计方向发展，大幅提高了设计效率与质量。概括介绍了铁路三维设计关键的理论研究及开发方法。     

浅论地理信息系统在航道管理中的应用

目前航道管理方法已相对落后,难以适应时代发展的要求,文章提出将GIS及网络等技术引入航道管理的观点,并就建立航道GIS对航道管理的作用做了初步的论述,以促进航道GIS的开发应用,达到航道电脑自动化管理的目的.     

Multimode trip information detection using personal trajectory data

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.