Data mining using regularized adaptive B‐splines regression with penalization for multi‐regime traffic stream models

This paper presents a new data mining method that integrates adaptive B‐spline regression and traffic flow theory to develop multi‐regime traffic stream models (TSMs). Parameter estimation is implemented adaptively and optimally through a constrained bi‐level programming method. The slave programming determines positions of knots and coefficients of the B‐spline by minimizing the error of B‐spline regression. The master programming model determines the number of knots through a regularized function, which balances model accuracy and model complexity. This bi‐level programming method produces the best fitting to speed–density observations under specific order of splines and possesses great flexibility to accommodate the exhibited nonlinearity in speed–density relationships. Jam density can be estimated naturally using spline TSM, which is sometimes hardly obtainable in many other TSM. Derivative continuity up to one order lower than the highest spline degree can be preserved, a desirable property in some application. A five‐regime B‐spline model is found to exist for generalized speed–density relationships to accommodate five traffic operating conditions: free flow, transition, synchronized flow, stop and go traffic, and jam condition. A typical two‐regime B‐spline form is also explicitly given, depending only on free‐flow speed, optimal speed, optimal density, and jam density. Copyright © 2013 John Wiley & Sons, Ltd.     

Traffic signal timing problems with environmental and equity considerations

Traffic signal timings in a road network can not only affect total user travel time and total amount of traffic emissions in the network but also create an inequity problem in terms of the change in travel costs of users traveling between different locations. This paper proposes a multi‐objective bi‐level programming model for design of sustainable and equitable traffic signal timings for a congested signal‐controlled road network. The upper level of the proposed model is a multi‐objective programming problem with an equity constraint that maximizes the reserve capacity of the network and minimizes the total amount of traffic emissions. The lower level is a deterministic network user equilibrium problem that considers the vehicle delays at signalized intersections of the network. To solve the proposed model, an approach for normalizing incommensurable objective functions is presented, and a heuristic solution algorithm that combines a penalty function approach and a simulated annealing method is developed. Two numerical examples are presented to show the effects of reserve capacity improvement and green time proportion on network flow distribution and transportation system performance and the importance of incorporating environmental and equity objectives in the traffic signal timing problems. Copyright © 2013 John Wiley & Sons, Ltd.     

Toll pricing framework under logit‐based stochastic user equilibrium constraints

This paper addresses the toll pricing framework for the first‐best pricing with logit‐based stochastic user equilibrium (SUE) constraints. The first‐best pricing is usually known as marginal‐cost toll, which can be obtained by solving a traffic assignment problem based on the marginal cost functions. The marginal‐cost toll, however, has rarely been implemented in practice, because it requires every specific link on the network to be charged. Thus, it is necessary to search for a substitute of the marginal cost pricing scheme, which can reduce the toll locations but still minimize the total travel time. The toll pricing framework is the set of all the substitute toll patterns of the marginal cost pricing. Assuming the users' route choice behavior following the logit‐based SUE principle, this paper has first derived a mathematical expression for the toll pricing framework. Then, by proposing an origin‐based variational inequality model for the logit‐based SUE problem, another toll pricing framework is built, which avoids path enumeration/storage. Finally, the numerical test shows that many alternative pricing patterns can inherently reduce the charging locations and total toll collected, while achieving the same equilibrium link flow pattern. Copyright © 2013 John Wiley & Sons, Ltd.     

临时关闭道路以疏导城市道路交通流的可行性分析

文章基于Braess悖论,以单位车辆的平均出行时间为最优目标,以网络道路系统为约束条件,从逆向思维角度建立了通过临时关闭道路引导交通流以缓解城市道路交通拥挤的Narsh均衡非线性规划模型,并以北京二环道路网络为研究对象进行了实例分析。结果表明,在城市道路网中,关闭部分路段并不能明显缓解交通拥堵现状,但可以在不影响路网整体通行时间的条件下改善路网局部拥堵状况。建议城市道路交通临时疏导方案应该根据路网交通流的分布特点和道路通过能力等因素进行制订。     

基于DELMIA/Robotics船体分段焊接仿真

随着造船自动化程度不断提高,工业机器人在船舶建造过程中的运用会越来越广泛。本文运用DELMIA/ Robotics对船体分段焊接过程中进行离线仿真。系统研究了复杂空间环境下机器人焊接过程中任务仿真、路径规划、碰撞干涉、离线编程等问题,验证了造船机器人焊接的可行性、安全性,提高造船自动化技术水平。     

长初级短次级六相直线感应电动机数学模型分析

基于大功率直线推进的特殊要求,设计了一种新型长初级短次级六相直线感应电动机,介绍了六相直线感应电动机的绕组结构。在此基础上,推导了六相长初级短次级直线感应电动机数学模型,分析了由电机各相端部绕组空间相对位置决定的六相直线感应电动机端部漏感的不对称规律,并与多相旋转电机端部漏感的规律进行了对比。建立了仿真模型,对电机的电磁性能进行计算。     

基于FPGA的高速串行通信在电力推进控制系统中的应用

本文介绍了一种优化时钟差分曼彻斯特编码的编码方法,并分析了其在高速串行通信中的实际应用价值。通过V H D L编程实现上述方法的编码、解码功能,完成了在Q u a r t u s I I环境下的仿真验证。通过在F P G A硬件设备上的实际测试,验证了其可行性和可靠性。通过对多种现有串行通信协议的研究,并在借鉴I E C 6 0 0 4 4-8通信规约的基础上提出了一种自定义协议。协议在编码效率和编程复杂度方面得到一定的平衡。通过与传统串行通信方式比较并结合电力推进控制系统的现状和性能需求,分析了其在电力推进控制系统中应用的可行性,并完成了实验验证。     

供电频率对单边直线感应电机性能的影响分析

针对基于单边直线感应电机（SLIM ）原理制成的钢板爬壁机器人平台,为了研究其供电频率对性能的影响及下一步的控制策略,本文使用Ansoft有限元分析法对直线感应电机供电频率进行了仿真计算,探讨了电机的推力,法向力,不同部位磁通密度以及次级铁心电流密度随之改变的幅度和规律,并结合M atlab计算了电机频率与功率因数的关系,得到了合理的结果,验证了样机设计的合理性和可行性,为下一步样机的优化设计提供了理论参考。     

基于分层控制的车辆主动制动稳定性分析

为提高车辆行驶的主动安全性,引入分层控制思想。建立名义横摆角速度和名义质心侧偏角为输出的线性二自由度车辆模型。基于线性二次型调节器设计上层控制器,得到附加横摆力矩,采用差动制动原理,设计中层控制器对附加横摆力矩进行分配,根据中层控制器分配的附加横摆力矩计算滑移率增量,基于PID控制理论设计下层滑移率控制器,以控制车轮的制动压力;最后联合MATLAB／Simulink和CarSim进行鱼钩转向和双移线转向仿真试验。结果表明,采用分层控制能够有效地提高车辆行驶的主动制动稳定性。     

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.