接触网检测车数据处理软件

本文通过分析接触网检测车数据处理系统的数据流向，数据组成及工作过程，确定了软件结构，软件功能及数据结构，同时讨论了软件设计中所采取的主要技术。     

基于闭环力反馈原理的非线性液压主动阻尼悬架模型的研究及优化设计

本文在理论上建立了一套基于主动力反馈原理的新型主动阻尼悬架的设计和优化方法。首先提出了该主动阻尼悬架的实现模型，该模型是在传统的液力减振器的基础上，应用半主动控制的思想，结合力反馈的原理建立起来的内部液压反馈阻尼网络模型。理论优化分析和计算机仿真表明，通过该液阻网络模型，即可以实现电控的主动阻尼悬架的功能，它可以根据汽车行驶路况的好坏，自适应地调节悬架阻尼大小以实现主动阻尼悬架的最优控制。     

Simulation studies of information propagation in a self-organizing distributed traffic information system

This paper investigates the feasibility of a self-organizing, completely distributed traffic information system based upon vehicle-to-vehicle communication technologies. Unlike centralized traffic information systems, the proposed system does not need public infrastructure investment as a prerequisite for implementation. Due to the complexity of the proposed system, simulation is selected as the primary approach in the feasibility studies. A simulation framework is built based on an existing microscopic traffic simulation model for the simulation studies. The critical questions for building the proposed market-driven system are examined both from communication requirements and traffic engineering points of view. Traffic information propagation both in freeway and arterial networks via information exchange among IVC-equipped vehicles is tested within the simulation framework. Results on the probability of successful IVC and traffic information propagation distance obtained from the simulation studies are generated and analyzed under incident-free and incident conditions for various roadway formats and parameter combinations. Comparisons between the speed of the incident information wave and the speed of the corresponding traffic shock wave due to the incident are analyzed for different scenarios as the most crucial aspect of the information propagation as a potential foundation for application in such a decentralized traffic information system.     

人工神经网络在故障诊断系统中的应用

人工神经网络以其独到的联想、记忆、储存和学习功能，在故障诊断领域受到了广泛关注。其中，BP网络是最成熟、应用最广泛的一种网络。但BP网络在实际应用中也存在诸多不足，如学习率的取值需要凭借经验或试算选取，网络学习收敛速度慢以及易陷入局部最小点等。针对BP网络的这些不足，采用了Rumelhan附加惯性冲量和动态调整学习率相结合的改进的BP网络方法，并尝试用该改进算法来对凝汽器进行故障诊断。事实证明，在平均情况下，本系统收敛速度远远快于基本的BP算法。     

专用铁路综合数据通信网建设方案探讨

铁路综合数据通信网,是铁路信息化建设的基础平台,建设时首先应完善铁路基础通信网,然后从业务需求、网络结构、技术方案、网络管理、安全策略等几个方面综合考虑,合理规划,有效实现资源最大化的目标,使专用铁路信息化建设更科学、更合理。     

用改进的前向神经网络预测铁路货运量

对影响铁路货运量的因素进行了分析。根据影响铁路货运量的诸因素的特点，介绍了一种改进的前向神经网络预测方法，并建立了铁路货运量前向神经网络预测模型。算例表明，其预测精度高于常规预测方法。     

基于人工神经网络的柴油机故障诊断

故障诊断是计算机模式识别领域的一个活跃课题。文中提出了基于人工神经网络的柴油机故障诊断方法，设计了适合该诊断系统的BP网络结构，并给出了一种基于黄金分割法改进的BP算法，用来自适应调整网络学习速率。仿真结果表明：该算法具有很快的学习速度和较高的学习精度，完全适用于柴油机故障诊断系统。     

高速公路常发性交通拥挤路段实时判定与跟踪研究

提出的自动判定和实时跟踪高速公路常发性拥挤路段的方法包含3个算法：①路段平均车速算法；②交通流区段类型判定算法；③排队类型判定算法。考虑采用两个前后相邻车辆检测站之间的路段平均车速来实时判定和跟踪常发性交通拥挤路段的情况，使得模型所提供的信息更能反映路段的真实交通状况。采用路段平均车速的方法克服了目前采用点速度来跟踪车队方法的局限性，并且采用“客观”标定临界车速作为基于现场数据判定车队状态的一种方法。     

Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types

The automotive industry is witnessing a revolution with the advent of advanced vehicular technologies, smart vehicle options, and fuel alternatives. However, there is very limited research on consumer preferences for such advanced vehicular technologies. The deployment and penetration of advanced vehicular technologies in the marketplace, and planning for possible market adoption scenarios, calls for the collection and analysis of consumer preference data related to these emerging technologies. This study aims to address this need, offering a detailed analysis of consumer preference for alternative fuel types and technology options using data collected in stated choice experiments conducted on a sample of consumers from six metropolitan cities in South Korea. The results indicate that there is considerable heterogeneity in consumer preferences for various smart technology options such as wireless internet, vehicle connectivity, and voice command features, but relatively less heterogeneity in the preference for smart vehicle applications such as real-time traveler information on parking and traffic conditions.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.