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.     

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.     

Urban walkability considering pedestrians’ perceptions of the built environment: a 10-year review and a case study in a medium-sized city in Latin America

ABSTRACT

Numerous methodologies measuring walkability have been developed over the last years. This paper reviews the Walkability Index (WI) literature of the last decade (2009–2018) and highlights some limitations in the current approaches. Only a few studies have evaluated walkability in Latin America, mainly in big cities but not in medium and small-sized cities in the region, which present their own urbanisation dynamics, security issues, sidewalk invasion problems, and poor planning. Furthermore, most WIs in the literature use objective mesoscale variables to assess walkability in a given area. This paper contributes to filling these gaps by generating new evidence from a medium-sized city in Latin America to question if characteristics of the built environment encourage walking trips, as found in the literature, are transferable among regions. The study also proposes a novel index comprised of microscale and mesoscale built environment variables to assess walkability using virtual tools and considering users’ perceptions. The WI estimation relies on ranking probability models. The results of the case study suggest that subjective Security and Traffic Safety are the most crucial factors influencing walkability in these kind of cities, which is different from what is found in the literature from cities in developed countries where Sidewalk Condition and Attractiveness are the most important factors. Security appeared to be strongly associated with a subjective dimension, represented by the fear of crime or perceived risk for crime, instead of the actual occurrence of crimes. This result evidences the importance of the physical attributes of the real world and how they are captured, judged, and processed by pedestrians. Then, regional transferability of WIs needs to be done carefully. Finally, results in this paper highlight the importance of microscale built environment characteristics in the WI formulation in these cities. Results are in line with other research in some cities of the region, which found that microscale variables such as pavement quality and presence of obstacles on the sidewalks are relevant components to promote walkability.     

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.     

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.