智能型全站仪轨道基准网数据采集程序开发

研究目的:解决高速铁路轨道基准网(TRN)数据采集中测量任务繁重、观测精度要求高等问题,从而提高野外测量工作的效率和质量。研究结论:基于徕卡智能型全站仪GeoCOM的ASCII接口方式和高速铁路轨道基准网的测量原理与方法,轨道基准网数据采集程序(TRN DAS)实现了数据采集的自动化、数字化和高效化,大幅提高了野外测量工作的效率和质量,并已在沪宁、沪杭等高速铁路工程中推广应用,使用效果良好,具有明显的经济效益。     

有线载波技术在汽车数据传输中的应用研究

研究了有线载波技术在汽车数据传输中的应用，通过汽车内的电力线同时实现了供电以及数据的安全传输。     

汽车传动系统机械故障诊断方法的研究

本文针对汽车传动系统主要零部件的特点，研究了基于振动信号的故障诊断方法，包括特征提取、模式分类和诊断决策全过程。     

发动机试验数据管理系统的开发应用

介绍了发动机试验数据库管理系统的工作原理、结构、功能，发动机试验数据的处理、绘图及试验报告的生成；对系统开发及开发过程中的若干难点进行论述。实践已验证了该系统的正确性、实用性和高效性。     

基于数据挖掘的城市机动车停车调查分析研究

进行城市机动车停车场规划前需要开展停车场调查,以得到停车场的基本特征参数、机动车进出记录和停车者的停车行为等信息。通过数据挖掘中的一些方法可以在数据库中挖掘出有用的信息。根据机动车进出停车场的记录计算一系列的停车指标,并借助多元统计分析中典型相关分析的方法,分析停车指标与停车场基本特征参数之间的关系。然后利用关联规则挖掘停车问询调查数据中的潜在信息,分析停车者的行为特征,以掌握更多的停车信息。     

XML技术在交通综合数据库中的应用研究

交通综合数据库是交通综合信息平台的核心和基础。本文首先分析了交通综合数据库的功能以及其所存储的信息类型,分析了XML技术在异构数据源之间进行数据共享和交换方面的优势以及目前许多国家和地区对XML在交通数据交换中的研究状况,在此基础上提出了基于XML技术的交通综合数据库的系统结构,重点研究了利用XML实现数据交换的理论与技术途径,通过在实际研发工作中的应用,结果表明XML技术在交通综合数据库中有良好的应用前景。     

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.     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.     

Highway voting system: Embracing a possible paradigm shift in traffic data acquisition

The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.