SMART: simulation model for activities, resources and travel

This paper proposes the development of an activity-based model of travel that integrates household activities, land use patterns, traffic flows, and regional demographics. The model is intended as a replacement of the traditional Urban Transportation Planning System (UTPS) modeling system now in common use. Operating in a geographic-information system (GIS) environment, the model's heart is a Household Activity Simulator that determines the locations and travel patterns of household members daily activities in 3 categories: mandatory, flexible, and optional. The system produces traffic volumes on streets and land use intensity patterns, as well as typical travel outputs. The model is particularly well suited to analyzing issues related to the Clean Air Act and the Intermodal Surface Transportation Efficiency Act (ISTEA). Implementation would, ideally, require an activity-based travel diary, but can be done with standard house-interview travel surveys. An implementation effort consisting of validation research in parallel with concurrent model programming is recommended.     

A conflict model and interactive simulator (FASTCARS) for predicting enroute driver behavior in response to real-time traffic condition information

This paper proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavioral choice under Advanced Traveler Information Systems (ATIS). The theoretical framework is based on conflict assessment and resolution theories popularized in psychology and applied to models of individual consumer behavior. It is posed that enroute assessment and adjustment is a reactionary process influenced by increased conflict arousal and motivation to change. When conflict rises to a level at which conflict exceeds a personal threshold of tolerance, drivers are likely to alter enroute behavior to alleviate conflict through either route diversion of goal revision. Assessment and response to conflict arousal directly relate to the driver's abilities to perceive and predict network conditions in conjunction with familiarity of network configurations and accessible alternate routes.Data collection is accomplished through FASTCARS (Freeway andArterialStreetTrafficConflictArousal andResolutionSimulator), in interactive microcomputer-based driving simulator. Limited real-world implementation of ATIS has made it difficult to study or predict individual driver reaction to these technologies. It is contended here that in-laboratory experimentation with interactive route choice simulators can substitute for the lack of real-world applications and provide an alternate approach to data collection and driver behavior analysis. This paper will explain how FASTCARS is useful for collecting data and testing theories of driver behavior.     

新型耐海水用钢OCrl7Ni12Mo3Cu3及其点蚀行为的显微研究

采用金相显微镜、X射线衍射、扫描电子显微镜（SEM）及扫描隧道显微镜（STM）研究了新型耐海水用钢的显微组织及其点蚀行为。试验结果表明，该钢经1050℃、保温2h固溶处理后，可得到单相的奥氏体组织，具有良好的抗点蚀性能。并通过STM的观测，得到了新型钢的超高分辨三维点蚀形貌图象，为该钢的开发应用奠定了技术基础。     

基于静力的智能桥梁结构智能计算方案的研究

智能土木 桥梁结构在线监测环境下的智能计算是结构智能化的必要条件。通过对该环境下两种基于静力的计算路线的分析比较,给出了一个集神经网络力学反问题解法和有限元力学正问题解法为一体的智能计算方案。方案通过对荷载(被定义为结构工作状态指标)的实时识别和实时分检,以及分检状态流的有限元分析,建立实时、在线的结构健康监测机制和准实时、在线的结构健康诊断机制。结合模型试验,对方案予以初步实现并进行了仿真验证,其仿真计算结果表明该方案是可行的,有必要对其加以研究和完善。     

单向压力作用下有初挠度矩形板的有效宽度和减缩有效宽度

本文应用von－karman大挠度薄板方程，对承受单向面内压力的有初挠度矩形板进行后屈曲特性分析（其中挠曲函数取为双博氏级数展开式的前三项）。用参数摄动法求解非线性代数方程组，得到最大压应力与平均压应力之间，平均压应力与平均应变之间的显式函数表达式。进而推导出有初挠度矩形板的有效宽度和减缩有效宽度公式，并着重分析了两者的不同。     

条码字符的模糊识别方法

在分析条码字符常规识别方法的基础上,针对因扫描倾斜角和扫描速度改度引起的首读率不高问题,提出了模糊识别方法。理论分析和抽样结果都表明,在硬件设备和印刷质量相同的条件下,该方法的识别首读率明显高于常规识别方法。     

An investigation of the reliability of real-time information for route choice decisions in a congested traffic system

This paper investigates the reliability of information on prevailing trip times on the links of a network as a basis for route choice decisions by individual drivers. It considers a type of information strategy in which no attempt is made by some central controller or coordinating entity to predict what the travel times on each link would be by the time it is reached by a driver that is presently at a given location. A specially modified model combining traffic simulation and path assignment capabilities is used to analyze the reliability of the real-time information supplied to the drivers. This is accomplished by comparing the supplied travel times (at the link and path levels) to the actual trip times experienced in the network after the information has been given. In addition, the quality of the decisions made by drivers on the basis of this information (under alternative path switching rules) is evaluated ex-post by comparing the actually experienced travel time (given the decision made) to the time that the driver would have experienced without the real-time information. Results of a series of simulation experiments under recurrent congestion conditions are discussed, illustrating the interactions between information reliability and user response.     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

基于网格交通状态分类的行程时间规律挖掘与计算

在出租车轨迹数据挖掘的基础上，本文提出基于网格交通状态的行程时间计算方法。在区域网格化的基础上，利用出租车全球定位系统(Global Positioning System, GPS)数据构建区域网格宏观基本图，并对宏观基本图的流量-密度关系进行拟合；进而使用高斯混合聚类法，将区域交通状态分类为畅通、轻度拥堵和重度拥堵。对不同交通状态网格的行程时间进行挖掘分析，发现3类交通状态下网格行程时间表现出不同的分布特征，畅通、轻度拥堵和重度拥堵的最佳行程时间分布分别为Gamma分布、Weibull分布和对数正态分布；通过不同状态网格行驶时间联合概率密度分布的近似拟合推导出路径网格行程时间概率密度模型。本文提出的方法可以快速计算一 定可靠度条件下的行程时间，对不同线路和时间内的案例分析结果表明，该方法对路径行程时间估计的平均绝对误差在1%~16%，可以为交通诱导与未来导航提供技术方法支撑。     

基于偏好序的高铁票价与售票时间窗联合优化

采用形式灵活且适用性好的偏好序模型刻画旅客购票选择行为，拓展现有高速铁路动态定价与售票控制策略联合优化的研究方法。按照票价变动范围划分客票等级，结合偏好序模型的特点，通过决策各等级客票售票时间窗进行售票控制，同时，在一定范围内优化各客票等级的票价，实现票价与售票控制的联合决策。本文在分析弹性客流的基础上，采用偏好序选择概率、 到达率和时间窗长度测算客票销售量。根据运营需要设定客票产品排序，以各客票产品停售时间和票价作为决策变量，建立以期望总收益最大为目标的非线性规划模型，并且将粒子群算法与线性规划精确求解嵌套求解模型。以京沪高铁为背景进行数值实验，结果表明：与固定各等级票价只优化售票时间窗的方案相比，联合优化方案可提升期望总收益约4.54%；不同需求水平下，联合优化方案的期望总收益均高于固定票价方案；期望总收益随着转移购买概率增加而增加，且不同转移概率取值下联合优化方案的期望总收益均高于固定票价方案。所提方案可为高铁列车动态定价和制定售票控制策略提供决策支持。