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。     

大跨径混凝土桥梁施工挠度监测数据采集的自动化方案

针对大跨径预应力混凝土桥梁施工挠度监测中数据采集工作量大的特点。提出了利用PC-E500电子记簿采集外业数据。利用Delphi语言编程将数据导入Excel表格并从Excel表格中提取数据的自动化方案。     

智能压路机控制系统数据通信的实现

介绍了CAN总线及CANopen协议的技术特点,详细阐述了基于CAN总线技术的智能压路机控制系统数据通信的实现,使之达到了智能压路机的设计要求.     

帕萨特B5轿车电路-自动变速器控制系统(Ⅰ)

在帕萨特B5轿车布线图的基础上改画成该车电路原理图——自动变速器控制系统,并介绍该系统的主要功能,自动变速器故障码及其读取方法,自动变速器测量数据块及其读取方法。     

基于BIM的设计管理

针对BIM技术应用的现状和现阶段国内企业的BIM设计管理工作中存在的问题,探索研究基于BIM设计特点的管理系统。通过构建高效的BIM软件设计工作环境、优化基于BIM设计特点的管理流程、建立设计项目数据框架的方式,达到BIM设计管理系统的高效性和协同性。     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.     

Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.     

智能船舶集成设计技术

针对符合CCS《智能船舶规范》入级符号的船舶设计开发,研究分析智能船舶集成设计技术的设计思想、设计要素、设计框架和关键技术,设计目标船解决方案,并讨论了未来需要突破技术方向。     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.