Dynamic traffic metering in urban street networks: Formulation and solution algorithm

Traffic metering offers great potential to reduce congestion and enhance network performance in oversaturated urban street networks. This paper presents an optimization program for dynamic traffic metering in urban street networks based on the Cell Transmission Model (CTM). We have formulated the problem as a Mixed-Integer Linear Program (MILP) capable of metering traffic at network gates with given signal timing parameters at signalized intersections. Due to the complexities of the MILP model, we have developed a novel and efficient solution approach that solves the problem by converting the MILP to a linear program and several CTM simulation runs. The solution algorithm is applied to two case studies under different conditions. The proposed solution technique finds solutions that have a maximum gap of 1% of the true optimal solution and guarantee the maximum throughput by keeping some vehicles at network gates and only allowing enough vehicles to enter the network to prevent gridlocks. This is confirmed by comparing the case studies with and without traffic metering. The results in an adapted real-world case study network show that traffic metering can increase network throughput by 4.9–38.9% and enhance network performance.     

基于前轮转角约束自适应模型预测控制的路径跟踪研究

针对在车辆行驶中较小的前轮转角无法充分利用路面附着能力,较大的前轮转角使得车辆的行驶稳定性差的问题,文章提出了一种前轮转角约束自适应模型预测方法。首先建立车辆的动力学模型,然后通过计算得到轮胎纵向力,最终得到车辆的前轮转角。将车辆的状态量与前轮转角自适应约束条件输入给模型预测控制器,输出车辆的前轮转角,实现对参考路径的跟踪。在Carsim和MATLAB平台上联合仿真,仿真结果表明前轮转角约束自适应模型预测控制的车辆相比固定转角约束的车辆具有较好的跟踪能力和稳定性。     

考虑移步需求的无桩型共享单车动态调度研究

共享单车系统自发的不均衡性导致单车数量分布与用户需求分布之间产生偏移，降低了系统服务能力，需要调度实现再平衡. 现有动态调度算法缺乏考虑起点车辆供给不足，用户在出行过程中“再次”取车的移步需求，难以准确识别用户真实的出行需求分布，降低了调度效果. 本文提出以用户出行选择行为为下层，以调度车辆路径规划为上层的双层规划模型框架，设计结合仿真系统的启发式求解算法. 算例场景基于上海市虹口、杨浦区共享单车历史出行数据搭建，并进行网格化处理. 算例结果表明，模型能有效识别移步需求，提高共享单车的供需匹配能力. 针对各类调度资源配置情况，共享单车的供需匹配率提升18.07%~ 19.89%，提高了共享单车系统的管理效率.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

高速路网不停车收费车道优化布设方法

合理设置高速公路收费站ETC (Electronic Toll Collection)车道数量，对高速公路通行效率至关重要。针对目前路网中ETC与MTC (Manual Toll Collection)车辆混行的情况，考虑ETC的普及率，结合多用户路网均衡模型和排队论方法，建立基于双层规划模型的高速路网ETC车道优化布设方法。上层模型以车辆总通行时间最小为目标，优化设置进出收费站的ETC车道数量；下层模型为多用户路网均衡模型，反映ETC和MTC车辆的路径和收费车道选择行为。下层模型通过设计收费站的等价拓扑结构，表征收费站的车道使用规则及车辆的收费车道选择行为，并采用排队论方法估计ETC和MTC车道的收费排队时间。根据模型的特点设计了基于主动集的启发式算法，利用参数二进制与拉格朗日函数法确定迭代下降方向，解决了下降方向与步长难以计算的问题；通过内嵌优化函数的方式，保证在主动集转化过程中上层约束均不会失效，且避免了迭代过程中的模型解退化问题。基于上海市绕城高速进行实证分析，结果表明：随着ETC普及率的提升，收费排队时长按照负指数趋势下降；与按比例布设ETC车道的方法相比，所提方法最高可降低57.4%的收费排队时间，且该方法可以避免ETC车道布设过多对于MTC车道通行能力挤压造成的负面效果。研究成果可以有效指导高速路网ETC车道的布设，提高路网通行效率。     

客运站到发线运用优化研究

为了合理编制客运站到发线运用计划,作者确定了到发线均衡性评价标准,建立了既满足到发线固定使用方案,又达到均衡使用到发线的多目标二次0-1规划模型;并以德州东站为实例,利用LINGO软件进行了求解与验证。     

物流中心选址与规模的层次优化模型

同时考虑生产商和分销商的利益,采用双层规划模型来描述物流中心选择以及规模问题.上层规划从生产商的角度出发,通过调整物流中心位置及规模使生产商的生产成本、运输成本和经营成本最小；下层规划从分销商的角度出发,通过调整从各个物流中心进货的数量使自身的平均运输成本最低.设计了基于遗传算法的求解方法,并用数值算例验证了模型和算法的有效性.     

Improving maritime inventory routing: application to a Brazilian petroleum case

The crude oil offloading and supply problem (COSP) is a type of operation maritime inventory routing (MIR) problem encountered by petroleum companies. In COSP, the company not only is responsible for the ship scheduling to carry the crude oil from production sites to discharge ports but also must maintain inventory levels at both ports (production and consumption) between safety operational bounds to avoid disruptions in its crude oil production and/or refining processes. We show how to improve significantly the decision-making process in a Brazilian petroleum company using a mixed-integer linear programming (MILP) model to represent COSP. Comparison tests with a current ship-scheduling method adopted in the company indicated that the use of the MILP model increased the transportation efficiency and reduced costs by 20% on average. In addition to the quantitative gains, the use of a MILP model to solve COSP has succeeded when encountering real-life events, such as variation in production or consumption rates, berth unavailability, and changes in the storage capacities at ports.     

高桥墩桩基础的优化设计及因素分析

在高桥墩桩基屈曲能量法分析的基础上,提出兼顾结构稳定、强度和经济性的优化模型,并应用优化算法中非线性序列二次规划法,进行优化设计探讨,同时给出优化设计算法流程图,编制了相应的优化计算程序;为了解桥墩高度、轴向荷载、地基比例系数和混凝土弹性模量对于高桥墩桩基屈曲的影响,结合优化设计模型进行了因素分析,探讨了参数变量对目标函数最优值的影响规律和程度,比较结果说明,在进行高桥墩桩基的屈曲受力分析时,高桥墩的大变形影响不容忽视;而一般在工程设计中,可考虑将桩周土体和混凝土弹性模量的增强作用作为设计的安全储备。