智能汽车自动紧急转向避撞的跟踪控制方法对比研究

为了提高智能汽车的主动安全性,提出3种不同的自动紧急转向避撞跟踪控制方法。首先建立汽车避撞简化模型,对制动、转向及两者相结合的3种不同避撞方式进行对比分析。其次,为深入研究汽车避撞过程中的实际响应,建立包含转向、制动及悬架3个子系统耦合特性的底盘18自由度统一动力学模型,并进行相关试验验证。随后构建智能汽车自动紧急转向避撞控制框架,对五次多项式参考路径和七次多项式参考路径的横摆角速度和横摆角加速度进行对比分析。接着以线性2自由度转向动力学模型为参考对象,对最优控制四轮转向、最优控制前轮转向、前馈与反馈控制相结合的前轮转向3种不同的跟踪控制系统分别进行设计。最后,以汽车底盘18自由度统一动力学模型为研究对象,对上述3种避撞控制系统进行仿真试验对比分析。研究结果表明：与制动避撞相比而言,转向避撞所需的纵向距离有较大降低,随着车速的增加和路面附着系数的越低,效果越明显;七次多项式参考路径比五次多项式参考路径的避撞过渡过程更为平缓,当实际车速与控制器所用车速不一致时,前者避撞性能表现更优;最优四轮转向控制系统在高、低2种不同附着路面都具有较好的避撞效果,最优前轮转向控制系统次之,而前馈与反馈相结合的前轮转向控制系统在低附着路面上则表现出严重的失稳。     

连续刚构桥双薄壁墩参数优化研究

考虑侧向风压产生扭转及温度变化产生纵向变形的作用，通过对双薄壁墩连续刚构桥的受力分析，探讨了双薄壁墩设计的影响因素，以非线性规划为基础，应用罚函数法对双薄壁墩参数进行优化研究。     

基于最优加权Steiner树的枢纽型物流中心选址问题

为了满足近年来物流运输业快速发展的需要,促进物流中转运输网络的合理化建设,研究了枢纽型物流中心的功能和选址原则,详细分析了影响枢纽型物流中心选址的各种因素,提出了基于结点带权的欧氏Steiner最优树的枢纽型物流中心选址方案.针对该方案设计了相应的智能优化算法,并进行了具体的程序实现.借助该方案不仅可以使总的运输成本最小,而且能够在无需事先确定备选点的数量和位置的情况下实现同时确定枢纽型物流中心的数量及位置的目标.最后以长三角地区枢纽型物流中心的建设问题为背景,对各种数据进行了仔细的分析比较,从中确定若干区域作为物流服务需求点集,并将各种因素的综合效用作为物流需求点的权值,对上述算法进行了有效性验证.     

基于Q-learning和BP神经元网络的交叉口信号灯控制

解决单交叉口信号灯最优控制问题。提出了基于强化学习的信号灯控制系统结构,应用强化学习中Q学习,将信号灯最优控制问题转变成是否切换运行相位的决策问题,提出了采用BP神经元网络实现Q学习的信号灯控制系统。应用微观交通仿真软件PARAMICS进行仿真分析,结果表明该系统能够感知交通流变化,并能够自适应地调整信号灯切换策略,以达到最优的控制效果,该方法是可行的,与定时控制相比具有明显的优势。     

混合交通流条件下区域交通信号控制优化模型

针对传统区域交通控制技术无法应对机非冲突干扰的问题, 结合中国城市道路混合交通流的特点, 研究了交叉口与路段非机动车对机动车的干扰。分析了区域路网机动车交通特征, 确定了混合交通特性相似的区域。基于路段非机动车的阻滞作用, 分析了交叉口通行能力的折减与相邻交叉口相位差的优化。以区域路网机动车总延误为优化目标, 建立了非机动车影响条件下的区域交通信号控制优化模型, 优化了信号周期时长、绿信比和相位差等参数, 并利用遗传算法求解模型。利用VISSIM仿真软件, 以上海市杨浦区五角场环形区域路网为例对优化模型进行验证。验证结果表明: 现状信号控制方案下区域路网7个交叉口机动车的车均延误为24.5~42.9s, 平均为35.99s, 路网总延误为256.39h, 优化后交叉口的车均延误为21.8~36.4s, 平均为30.12s, 路网总延误为214.57h, 7个交叉口车均延误减少了10%~24%, 平均为16.31%。可见, 优化模型能够显著降低区域路网车均延误与总延误, 提高区域路网通行效率。     

考虑楼板变形时框支剪力墙结构的框支层合理设计研究

将框支剪力墙结构沿竖向划分成为转换层上部剪力墙结构和转换层下部框架-剪力墙结构。对各部分结构假定质量刚度沿高度均匀分布,将楼板看成水平放置的深梁,并假定以剪切变形为主。根据平衡条件,列出微分方程组。采用相应的数学程序求解,得到结构水平侧移及各构件内力。通过对内力和侧移的双重控制,进行框支层的合理设计。     

地铁网络服务韧性评估与最优恢复策略

针对已有基于拓扑效率的地铁网络韧性指标无法反映地铁运营实际的不足,构建考虑线 路流量影响的路网服务效率指标和基于服务效率的路网服务韧性指标,以及基于路网服务效率 的节点重要度指标;提出以路网服务韧性最大化为目标的优化模型,并基于遗传算法求解模型获 得最优恢复策略。算例结果表明：分别以服务效率和拓扑效率作为路网性能指标,获得的失效节 点恢复次序明显不同;蓄意攻击下,最优恢复策略获得的路网服务韧性分别比基于重要度的优先 恢复策略、基于节点度的优先恢复策略和随机恢复策略高16.76%、72.11%和86.21%。上述结果 表明,必须根据地铁运营实际合理选择路网性能指标和恢复策略,否则可能得到次优甚至明显偏 离实际的方案,无法实现预期目标。     

Nonlinear pricing in linear cities with elastic demands

Nonlinear road pricing charges each traveler based on his/her trip’s corresponding particular attribute level. In order to help authorities in designing road pricing systems at a strategic level, this paper attempts to address two fundamental questions: (i) what is the value of pricing’s nonlinearity for mitigating traffic congestion? (ii) if a nonlinear toll function is implemented, should it be convex, concave or other shape? Specifically, we consider distance-based pricing in linear cities. For linear monocentric cities with heterogeneous travelers, we show that the system optimal distance-based pricing indeed exhibits nonlinearity. It is proved that: (i) the cost-based system optimal toll function is monotonically increasing and concave with respect to the traveled distance; (ii) the time-based system optimal toll function always exists and is unique. If the initial proportion of each traveler group is invariant along a corridor and the demand function is of exponential type, then the time-based system optimal toll function enables the travelers, living further away from a city center, to face a lower toll level per unit distance. For a linear polycentric city, we demonstrate: (i) there always exists the system optimal differentiated (in terms of city centers) toll functions; (ii) it is highly possible that the system optimal non-differentiated toll function does not exist. Hence, we further propose an optimal toll design model, prove the Lipschitz continuity of its objective and adopt a global-optimization algorithm to solve it.     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.