我国汽车金融业的发展现状及趋势展望

首先分析了国内外汽车金融业的发展状况，并从当前我国汽车金融业存在的问题入手，着重从内部和外部环境2个方面进行了分析，从而有针对性地提出了我国汽车金融服务的改革思路和解决相关问题的政策建议，并对未来发展趋势作出理性预测和判断，制度设计上更趋于合理、完善；服务质量和水平逐步与国际接轨；竞争推动下的具有中国特色的金融创新渐趋活跃。在此基础上笔者设想了一种银行一体化的营销模式，权当为未来银行汽车金融业务的开展作一有益尝试。     

关于集成化汽车故障诊断系统及其支持技术研究

集成化汽车故障诊断系统有两种不同的体系结构,可由状态检测、信息融合、故障诊断、网络信息获取以及网络通讯等模块构成。在系统分析各模块功能得基础上,论述了检测设备仪器通讯的标准化、故障诊断专家系统的通用化、故障诊断推理方法的多元化和故障诊断知识获取的网络化是实现集成化汽车故障诊断系统的关键性支持技术。     

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。     

汽车操纵稳定性的广义汽车转向稳态响应模型

针对传统汽车操纵稳定性理论的线性二自由度模型的不足,构建了一个新的广义汽车转向稳态响应模型,并说明了该模型的广泛适用性。以某不足转向轿车模型的有关参数为例,仿真计算并比较了应用两种模型时该轿车等速圆周行驶时的稳态响应状况,验证了广义模型的正确性,同时得到了一条有别于传统汽车操纵稳定性理论的重要结论。     

基于模糊神经网络的既有钢筋混凝土桥梁构件可靠性评估

介绍了模糊神经网络的基本理论知识,根据其知识体系和既有桥梁可靠性的研究特点,结合相关文献的规定和研究成果,引入与所检测的截面裂缝宽度、应变值和应变值相关的评估参数Z,利用既有钢筋混凝土桥梁养护等级标准给出了构件技术状况等级与可靠指标的对应关系,然后根据模糊神经网络的理论优势,对输入变量(实测数据:裂缝宽度,应变值和应变值)进行训练,计算出构件在不同荷载作用下的可靠指标,其可用于既有桥梁结构构件可靠性评估中。并结合一实桥拆除构件的试验结果,得到了一些有益的结论,验证了本方法应用于既有钢筋混凝土桥梁构件可靠性评估的可行性。     

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。     

Spatiotemporal influence of land use and household properties on automobile travel demand

Understanding the patterns of automobile travel demand can help formulate policies to alleviate congestion and pollution. This study focuses on the influence of land use and household properties on automobile travel demand. Car license plate recognition (CLPR) data, point-of-interest (POI) data, and housing information data were utilized to obtain automobile travel demand along with the land use and household properties. A geographically and temporally weighted regression (GTWR) model was adopted to deal with both the spatial and temporal heterogeneity of travel demand. The spatial-temporal patterns of GTWR coefficients were analyzed. Also, comparative analyses were carried out between automobile and total person travel demand, and among travel demand of taxis, heavily-used private cars, and total automobiles. The results show that: (I) The GTWR model has significantly higher accuracy compared with the Ordinary Least Square (OLS) model and the Geographically Weighted Regression (GWR) model, which means the GTWR model can measure both the spatial and temporal heterogeneity with high precision; (II) The influence of built environment and household properties on automobile travel demand varies with space and time. In particular, the temporal distribution of regression coefficients shows significant peak phenomenon; and (III) Comparative analyses indicate that residents’ preference for automobiles over other travel modes varies with their travel purpose and destination. The above findings indicate that the proposed method can not only model spatial-temporal heterogeneous travel demand, but also provide a way to analyze the patterns of automobile travel demand.     

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.     

Time-variant bulk carrier reliability analysis in pure bending intact and damage conditions

Time-variant reliability analysis of a corroded bulk carrier in intact and damage conditions is performed by First-Order (FORM), Second-Order (SORM) Reliability Methods and Importance Sampling simulation. Annual failure probabilities are determined up to 25-year ship lifetime, accounting for time-variant corrosion wastage of structural members contributing to hull girder strength. Statistical properties of hull girder capacity are determined by Monte Carlo simulation, applying three correlation models among corrosion wastages of structural members contributing to hull girder strength, namely no correlation, full correlation and full correlation among wastages of structural members belonging to the same category of compartments. A modified incremental-iterative method is applied, to account for instantaneous neutral axis rotation, in case of asymmetrical damage conditions, as for collision and grounding events. Incidence of intact/damage condition, as well as correlation among corrosion wastages, on annual sagging/hogging time-variant failure probability is investigated and discussed. Time-variant sensitivity analyses for intact and damage conditions are also performed, to investigate the incidence of random variables' uncertainties on the attained failure probability. Finally, the bulk carrier section scheme, benchmarked in the last ISSC Report, is applied as test case.