Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

A driving cycle detection approach using map service API

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method’s ability to detect vehicle mode accurately.     

A scalable communication middleware for real-time data collection of dangerous goods vehicle activities

Recently, real-time monitoring of Dangerous Goods Transport has drawn a lot of attention, thanks to its capability to provide a better visibility on dynamically moving vehicles, particularly through a Web Mapping application. Yet, one of the challenges to be faced designing such a system is an effective architecture for real-time collection of telemetry and event data conveyed by the vehicle on-board system, such the Global Positioning System coordinates. In this paper, we have focused on optimizing the process for managing a large quantity of data transmitted via network sockets that use the Transmission Control Protocol. Then we prove the process efficiency through performance and scalability tests. The middleware is being implemented as a part of a project that aims to monitor the Italian petrochemical company Eni’s oil trucks shipment along Europe and USA territories.     

内河船舶自动导航系统研究

提出内河船舶自动导航系统,从技术上探讨该系统的可行性,诣在供有意实现内河船舶驾驶自动化的有关人员参考.     

城市公交智能信息系统发展研究

本文简要论述了城市公交智能信息系统的构成及其实施的必要性,分析了各子系统的主要作用及相互关系,在通常采用的公交智能信息系统的基础上又提出了一套新的开发思路,同时对所涉及的相关的关键技术如GPS、GIS 等作了简要的介绍和分析。     

GPS/DR组合定位技术在物流运输监控管理中的应用

车载定位系统的精度及可靠性是构建物流运输监控系统的首要目标.文中阐述GPS技术和DR技术的基本原理、各自的优缺点及其组合定位技术优势,并对两种技术数据融合方法进行研究.在介绍物流运输监控管理的基础上研究基于GPS/DR组合定位技术物流监控系统的构建方案,对该系统功能作了详细介绍,并描述物流监控管理调度流程.最后对基于GPS/DR组合定位技术在深圳市物流运输监控管理中的应用情况进行了介绍.     

鲜河断裂及两侧地块的GPS监测

对鲜水河断裂及两侧地块的GPS观测,得到欧亚框架下运动速度场.基于得到的运动速度,采用刚性地块假设下的最小二乘拟合方法,得到川滇地块与川青地块的运动速度分别为19.5±3.4和13.2±3.4 mm/a,川滇地块相对川青地块的运动速度为8.3±3.4 mm/a,鲜水河断裂的运动速度为7.9～8.8 mm/a(甘孜-乾宁段为8.8±3.5 mm/a,乾宁-康定段为7.9±3.3 mm/a).上述研究结果表明,鲜水河断裂、川滇地块与川青地块所围成的区域为重要构造活动区.     

GPS/DR组合导航车载单元的开发研究

随着城市车辆的急剧增长和道路交通状况的拥挤,以全球定位系统（GPS）定位技术为主的民用车辆的定位导航和监控技术得到了飞速发展.通过研究GPS和航位推算系统（DR）的组合导航技术,给出了GPS/DR组合导航系统车载单元的设计方案.由GPS接收机、压电陀螺仪、DVD、单片机、手机模块、车载里程仪、工控微机组成的车载单元具有车辆自动导航和监控报警功能.     

基于GPS技术的大跨桥梁实时动态监测系统

大跨度桥梁,特别是主梁为钢结构的跨江大桥,其变形受台风、地震、车辆以及温度变化等因素的影响很大,对其进行实时动态的变形监测,能够获取桥梁在各种荷载作用下的变形数据,了解其工作状况,进一步掌握其变化规律。以江阴大桥结构健康监测系统中的GPS桥形在线监测系统为例,详细介绍了GPS监测系统的组成和数据分析,实践证明,该系统能够精确地记录大桥在车辆、风荷载以及温度变化情况下结构主梁和主塔的位移特征,从而为验证结构抗风、抗震设计和大桥的日常安全维护提供可靠依据。