基于神经网络和混合编程的整流桥故障诊断研究

本文介绍了采用VC＋＋和MATLAB混合编程实现对三相整流桥进行故障诊断的方法。该方法通过VC＋＋调用MATLAB引擎来驱动神经网络,将神经网络、数据采集及人机接口结合起来,对三相整流桥的故障进行在线诊断。试验证明,经过足够的样本训练,采用三层BP神经网络能达到一定的准确度。     

考虑多目标的城市停车换乘选址优化模型及算法

停车换乘选址问题是城市交通网络设计研究的重点领域,已有研究的优化目标多集中在系统总费用方面,而对交通可持续发展方面考虑不足。为此,提出综合考虑多方面目标的停车换乘设施选址优化模型及其求解算法。首先,基于超网络理论,提出多方式城市交通系统的超网络模型并定义O-D (Origin-destination)间的超路径、有效超路径及子路径,结合出行者出行过程及交通网络拥挤特征,给出超路径费用的数学表达;其次,基于多方式交通网络随机均衡配流结果,构建交通总阻抗、污染物排放量以及交通系统公平性等系统优化指标的计算模型,并建立用以描述停车换乘设施选址问题的多目标优化模型;进而,以多目标系统优化模型为上层问题,以超网络下满足Logit分配的多方式交通网络配流模型为下层问题,构建描述城市多方式交通系统停车换乘设施选址问题的双层规划模型,并基于模型特征,结合“记录-搜索”思想设计非支配排序遗传算法进行求解;最后,基于Sioux Falls网络设计算例。研究结果表明：算法能够在有限的步骤内搜索到90%以上的Pareto最优解;平均而言,停车换乘措施使得交通总阻抗减小了0.31%,污染物排放量减少了7.32%;被优化的3个目标之间无直接关联,说明将停车换乘选址问题建立为多目标模型是必要的。模型与算法可为现实城市中的停车换乘设施选址优化设计提供解决思路。     

Multiple criteria optimization method for the vehicle assignment problem in a bus transportation company

A vehicle assignment problem (VAP) in a road, long‐haul, passenger transportation company with heterogeneous fleet of buses is considered in the paper. The mathematical model of the VAP is formulated in terms of multiobjective, combinatorial optimization. It has a strategic, long‐term character and takes into account four criteria that represent interests of both passengers and the company's management. The decision consists in the definition of weekly operating frequency (number of rides per week) of buses on international routes between Polish and Western European cities. The VAP is solved in a step‐wise procedure. In the first step a sample of efficient (Pareto‐optimal) solutions is generated using an original metaheuristic method called Pareto Memetic Algorithm (PMA). In the second step this sample is reviewed and evaluated by the Decision Maker (DM). In this phase an interactive, multiple criteria analysis method with graphical facilities, called Light Beam Search (LBS), is applied. The method helps the DM to define his/her preferences, direct the search process and select the most satisfactory solution.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Energy savings in transport

A decade of increasing Federal attention to urban transportation needs has culminated in the 1970 Urban Mass Transportation Assistance Act. This Act is intended to provide 10 thousand million dollars over the next 12 years in Federal assistance money to urban public transportation systems. This paper examines the needs of selected U.S. cities as a basis for (1) understanding the vast, various and complex transportation needs of urban areas throughout the country, and (2) assessing the sufficiency of these funds. The sample cities have been placed into three broad categories based on the state of development of their transportation systems. In Category I cities, the essential need is to ensure the survival of bus systems for the use of non‐drivers, or to provide some other viable alternative to the automobile; in Category II cities, the primary needs are to relieve auto congestion and to improve public transportation components, while in Category III cities, the primary need is massive investment to improve and to extend public transportation facilities. It is concluded that the presently intended Federal funding level for transportation will not meet the financial requirements of the Category III cities.     

Mathematical properties and constraints representation for bottom-up approaches to the evaluation of GHG mitigation policies

Bottom-up models, including MARKAL, MESSAGE and AIM, are widely used when analyzing the effect of greenhouse gas (GHG) abatement policies. These bottom-up models are mostly formulated as a linear programming (LP) optimization model to find both the minimal cost combination of abatement technologies and energy flows while satisfying demands. It is not unusual that the bottom-up modeling involves a great number of technical, industrial, socioeconomic and environmental constraints. Investigating representative constraints needed for analyzing GHG abatement policies, this study proposes how to implement these constraints in bottom-up modeling.     

A mathematical programming technique for matching time-stamped records in logistics and transportation systems

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.     

Scheduling of Intelligent and Autonomous Vehicles under pairing/unpairing collaboration strategy in container terminals

A new class of Intelligent and Autonomous Vehicles (IAVs) has been designed in the framework of Intelligent Transportation for Dynamic Environment (InTraDE) project funded by European Union. This type of vehicles is technologically superior to the existing Automated Guided Vehicles (AGVs), in many respects. They offer more flexibility and intelligence in maneuvering within confined spaces where the logistic operations take place. This includes the ability of pairing/unpairing enabling a pair of 1-TEU (20-foot Equivalent Unit) IAVs dynamically to join, transport containers of any size between 1-TEU and 1-FFE (40-foot Equivalent) and disjoin again. Deploying IAVs helps port operators to remain efficient in coping with the ever increasing volume of container traffic at ports and eliminate the need for deploying more 40-ft transporters in the very confined area of ports. In order to accommodate this new feature of IAVs, we review and extend one of the existing mixed integer programming models of AGV scheduling in order to minimize the makespan of operations for transporting a set of containers of different sizes between quay cranes and yard cranes. In particular, we study the case of Dublin Ferryport Terminal. In order to deal with the complexity of the scheduling model, we develop a Lagrangian relaxation-based decomposition approach equipped with a variable fixing procedure and a primal heuristics to obtain high-quality solution of instances of the problem.     

城市轨道交通高峰线路客流协同控制方法

在线路客流控制中，需同时考虑各个车站控流方案的可执行性与协同性. 采用 Fisher 最优分割法确定合理客流控制时段，基于此建立以乘客总等待时间最少和旅客周转量最大为目标的线路客流协同控制线性规划模型. 基于成都地铁2 号线AFC数据进行实验，针对协同控流与非协同控流方案，以及不同客流控制时段划分方案下的协同控流方案进行对比实验. 算例中：协同控流方案在旅客周转量下降约1.0%的情况下，乘客总等待时间减少约 56.7%；基于Fisher 最优分割法确定的时段划分方案中协同控流方案在乘客总等待时间方面最优，并具有很好的可执行性.     

灾后恢复阶段多期道路交通网络重建规划

灾后路网重建规划可分为应急阶段和全面恢复阶段。文中建立了灾后恢复阶段多期路网重建规划的双层模型，并采用灵敏度分析方法进行求解。上层模型考虑到建设资源的限制，以工程总效益最大为目标建立多期工程效益模型；下层模型是基于出行时间可靠性的用户均衡分配模型。最后结合实际路网进行了算例分析，证明模型和算法是有效的。