带时空相关性分析的行车时间估计模型

基于流体动力学方程的行车时间估计模型不能很好地反映真实的行车时间,需要对其进行一定的改进.在对交通流进行流体动力学建模的基础之上,引入对高速公路路网中不同路段之间的行车时间相关性和同一路段不同季节、不同时段的行车时间相关性分析,建立了带时空相关性分析的时间估计模型,使用统计学的方法消除动力学模型的误差.     

高速公路网络上行车时间估计和最优路线的选取

首先建立交通流动力学模型求解问题Ⅰ.在不考虑流量和考虑流量的两种情况下,该模型都能够解出在任意给定的时刻t位于第一个传感器的车辆到达第5个感应器的行车时间.我们还从四个方面给出了判断交通堵塞的衡量标准,并且利用神经网络方法准确地对未来的车流状态进行了预测.问题Ⅱ建立了交通网络的加权有向图模型,引入协方差矩阵描述网络中道路之间的相关性,并设计了查找最优路径的动态Dijkstra算法.问题Ⅲ构建了统计多目标规划模型,利用车比雪夫不等式,成功找到了从端点3到14和14到3的最优路径,并估算出了对应的行车时间.     

基于模拟退火算法的知情交易研究北大核心CSCD

为了更准确地估计知情交易概率水平(PIN),文章主要使用模拟退火算法并结合交易量时间概念对知情交易模型进行参数估计.算法改进后的知情交易模型已可适用于较高频的交易订单数据,从而更好地捕捉微观市场中PIN的变化.参数估计方面,文章主要使用模拟退火算法对EKOP-PIN、EHO-PIN以及DY-PIN进行带约束的最大似然估计,并将结果与无约束优化的间接估计算法进行了比较,基本解决了以往算法存在的局部最优和数值溢出等问题.数值模拟显示,使用模拟退火算法并运用模式搜索法进行末端优化的混合算法在计算PIN时能够进一步提高估计精度.使用沪深300股指期货高频交易数据的实证结果显示,重新估计的EHO-PIN对波动率有着较高的解释和预测能力,可作为一种有效的市场波动率预警指标.     

基于模拟退火算法的知情交易研究

为了更准确地估计知情交易概率水平(PIN),文章主要使用模拟退火算法并结合交易量时间概念对知情交易模型进行参数估计.算法改进后的知情交易模型已可适用于较高频的交易订单数据,从而更好地捕捉微观市场中PIN的变化.参数估计方面,文章主要使用模拟退火算法对EKOP-PIN、EHO-PIN以及DY-PIN进行带约束的最大似然估计,并将结果与无约束优化的间接估计算法进行了比较,基本解决了以往算法存在的局部最优和数值溢出等问题.数值模拟显示,使用模拟退火算法并运用模式搜索法进行末端优化的混合算法在计算PIN时能够进一步提高估计精度.使用沪深300股指期货高频交易数据的实证结果显示,重新估计的EHO-PIN对波动率有着较高的解释和预测能力,可作为一种有效的市场波动率预警指标.     

行车时间估计与路径寻优问题

通过公路上安装的传感器采集数据,从而估算出车辆的行驶时间并求取最优路径,可以方便人们的出行.根据某段公路上传感器提供的数据分析了该段公路的交通状况,建立微分方程模型和关联度分析模型分析交通状况特征及相互影响,并利用ARIMA模型对速度进行了预测.通过对交通干线图进行分析,在假定各路段上的运行时间为独立的随机变量、考虑路段间的相互影响和根据给定的条件这三种情况下,分别建立模型用于估计跟路段通过时间和寻找最优路径,求解得到理想的结果.所建立模型有较强的实用性,有一定的参考作用.     

基于Stackelberg博弈的时变双层交通分配模型

提出一个时变双层交通分配模型,其中上层网络管理者设立了一个路段的最大排队长度,其目标是使由网络流和排队长度定义的总出行时间最小.目标函数在离散时段内以路段流量和排队长度作为决策变量,同时考虑不同类型的信号交叉口延误的影响.下层网络用户的反应依赖于上层管理者的决策,其选择是使自身感知阻抗最小的路径,服从一个基于成对组合Logit的路径选择模型,构成一个成对组合Logit的均衡分配问题.结合了交通分配和流传播方法,将其表示为一个均衡约束下的双层数学规划问题,形成了一个Stackelberg非合作博弈.使用遗传算法求解该双层规划问题,并采用实证分析来表现模型的特征和算法的计算表现.结果表明路径重叠、路段流量、路段排队长度等因素对网络均衡流分布均有显著影响.     

部分状态可见的隐马尔可夫模型的Viterbi算法

本文研究了隐马尔可夫模型的Viterbi算法,在已知隐马尔可夫模型的部分状态、初始概率分布、状态转移概率矩阵和观测概率矩阵的条件下,由此Viterbi算法给出最优状态序列的估计.相对于已有的算法,本文的算法考虑了部分可见状态对初始条件和递推公式的影响,并且本文的算法能保证预测的状态序列是整体最优的.最后,我们将本文的算法应用于故障识别,从而验证所设计算法的可行性.     

基于改进蚁群算法的带硬时间窗的接送机场服务路径优化研究

针对航空票务公司免费接送顾客去机场路径优化的问题,文章研究了更贴近实际的关于单时间窗约束下的接送机场服务,同时考虑了接送过程中的碳排放,构建出相应的优化模型,提出利用蚁群算法来解决该问题,并采用改进的蚁群算法加以求解.在初始选择路径上的改进,有效解决路径选择上容易陷入局部最优的缺点;根据当前节点到目标点和起点的距离,重新设计启发式函数,驱使车辆尽量沿着起点和目标点之间的最短路行进;依据实时路径长度,动态调整挥发系数,精炼搜索空间,提高收敛性能.最后通过参数校验和实例计算验证,得出了适用于此问题的蚁群算法的参数优化组合;以及顾客点位置在三种不同类型分布下时,使用改进后的蚁群算法都能更好的求出问题的最优解,表明改进后的蚁群算法是解决航空票务公司免费接送顾客去机场服务路径优化问题的一个更有效的求解算法.     

基于改进蚁群算法的无人仓的多AGV避碰路径优化策略

对无人仓库中多AGV系统的避碰路径优化问题进行了研究,提出了一种基于弹性时间窗和改进蚁群算法的多AGV避碰路径优化策略.通过对传统蚁群算法改进启发式信息和信息素更新策略,来提高算法的执行速度和寻优能力,提出AGV任务优先级排序并改进冲突解决策略来解决多AGV之间的不同路径冲突.基于电商物流无人仓库的环境,利用MATLAB仿真软件对多AGV避碰路径规划进行建模分析.实验结果表明,基于弹性时间窗和改进蚁群算法的可以实现多AGV避碰路径规划,并能够短时间内找到避碰最优路径.     

铁路应急设施非概率可靠性选址优化研究

为解决小样本、贫信息下铁路应急资源储备点的可靠性选址问题,创新性地将选址-路径问题与区间非概率可靠性方法结合起来,考虑灾情发生后应急设施点在可接受的时间范围内响应受灾点的需求能力及其稳定程度,采用区间值度量路段阻抗,基于区间非概率可靠性理论及区间运算规则,提出路径的非概率可靠性度量及可靠最短路径选择方法;建立基于区间时间阻抗下可靠最短路径的无容量设施选址模型,提出约束条件限制的Monte Carlo改进算法,确定了铁路资源储备点选址的最优方案。实例表明,本文的优化方案能更好地保证救援的时间可靠性,改进的求解算法具有更小的时间复杂度,有效地缩短了运算时间,改善了解的质量。本文的方法与模型体系对于实现铁路应急设施可靠性选址,为决策者提供决策支持,提高铁路应急响应能力具有重要的指导意义。     

基于交通流的多模糊时间窗车辆路径优化

研究了基于交通流的多模糊时间窗车辆路径问题,考虑了实际中不断变化的交通流以及客户具有多个模糊时间窗的情况,以最小化配送总成本和最大化客户满意度为目标,构建基于交通流的多模糊时间窗车辆路径模型。根据伊藤算法的基本原理,设计了求解该模型的改进伊藤算法,结合仿真算例进行了模拟计算,并与蚁群算法的计算结果进行了对比分析,结果表明,利用改进伊藤算法求解基于交通流的多模糊时间窗车辆路径问题,迭代次数小,效率更高,能够在较短的时间内收敛到全局最优解,可以有效的求解多模糊时间窗车辆路径问题。     

城市交通多类型随机用户出行决策模型研究

考虑城市交通中有多类出行者的一般情况,在ATIS等交通信息系统的影响下,不同类型的出行者对路径出行时间有不同理解,用不同的参数来反映.在此基础上,给出考虑路径选择、方式选择、讫点选择及是否出行的多类型随机用户出行决策模型,证明了模型的一阶条件满足路径选择、方式选择、讫点选择及是否出行的条件,最后给出模型算法.     

Integrating link-based discrete credit charging scheme into discrete network design problem

In this paper, we present an optimization model for integrating link-based discrete credit charging scheme into the discrete network design problem, to improve the transport performance from the perspectives of both transport network planning and travel demand management. The proposed model is a mixed-integer nonlinear bilevel programming problem, which includes an upper level problem for the transport authority and a lower level problem for the network users. The lower level sub-model is the traffic network user equilibrium (UE) formulation for a given network design strategy determined by the upper level problem. The network user at the lower level tries to minimize his/her own generalized travel cost (including both the travel time and the value of the credit charged for using the link) by choosing his/her route. While the transport authority at the upper level tries to find the optimal number of lanes and credit charging level with their locations to minimize the total system travel time (or maximize the transportation system performance). A genetic algorithm is used to solve the proposed mixed-integer nonlinear bilevel programming problem. Numerical experiments show the efficiency of the proposed model for traffic congestion mitigation, reveal that interaction effects across the tradable credit scheme and the discrete network design problem which amplify their individual effects. Moreover, the integrated model can achieve better performance than the sequential decision problems.     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

Dynamic system optimal performances of shared autonomous and human vehicle system for heterogeneous travellers

ABSTRACT

Autonomous vehicles (AV) can solve vehicle relocation problems faced by traditional one-way vehicle-sharing systems. This paper explores the deterministic time-dependent system optimum of mixed shared AVs (SAV) and human vehicles (SHV) system to provide the benchmark for the situation of mixed vehicle flows. In such a system, the system planner determines vehicle-traveller assignment and optimal vehicle routing in transportation networks to serve predetermined travel demand of heterogeneous travellers. Due to large number of vehicles involved, travel time is considered endogenous with congestion. Using link transmission model (LTM) as a traffic flow model, the deterministic time-dependent system optimum is formulated as linear programming (LP) model to minimize the comprehensive cost including travellers’ travel time cost, waiting time cost and empty vehicle repositioning time cost. Numerical examples are conducted to show system performances and model effectiveness.     

A dynamic route guidance system based on real traffic data

Mobility is one of the vital goods of modern societies. One way to alleviate congestion and to utilise the existing infrastructure more efficiently are Advanced Traveller Information Systems (ATIS). To provide the road user with optimal travel routes, we propose a procedure in two steps. First on-line simulations supplemented by real traffic data are performed to calculate actual travel times and traffic loads. Afterwards these data are processed in a route guidance system which allows the road user an optimisation with regard to individual preferences. To solve this multiple criteria optimisation problem fuzzy set theory is applied to the dynamic routing problem.     

道路维修施工路段交通分流信控优化模型

针对道路维修施工期间常采用的部分路面封闭施工且利用辅路进行分流的情形,探讨了交通分流信控优化模型。借助交通流波动理论,分析了施工路段及其前后车流拥挤排队及疏散特征和规律,分析了对车流进行控制需满足的约束,并分析了车流的延误计算公式。以总的车辆行驶时间最小化目标,原路径及分流路径的绿时分配及信号周期为优化参数,考虑交通分流控制的各种约束,建立了道路施工路段交通分流信控优化模型。分析了该模型属于非凸问题,因此提出了一种近似求解最优解的办法。通过一个示例对模型和求解算法进行了验证,并对一些规律性结果进行了分析。     

Solving the minimum toll revenue problem in real transportation networks

As a means to relieve traffic congestion, toll pricing has recently received significant attention by transportation planners. Inappropriate use of transportation networks is one of the major causes of network congestion. Toll pricing is a method of traffic management in which traffic flow is guided to proper time and path in order to reduce the total delay in the network. This article investigates a method for solving the minimum toll revenue problem in real and large-scale transportation networks. The objective of this problem is to find link tolls that simultaneously cause users to efficiently use the transportation network and to minimize the total toll revenues to be collected. Although this model is linear, excessive number of variables and constraints make it very difficult to solve for large-scale networks. In this paper, a path-generation algorithm is proposed for solving the model. Implementation of this algorithm for different networks indicates that this method can achieve the optimal solution after a few iterations and a proper CPU time.     

遗传算法与蚂蚁算法的融合在带时间窗的车辆路径问题中的应用

对带时间窗的车辆路径问题(VRPTW)的求解分为两个过程,先由遗传算法求解出初步的可行解,由此生成信息素初始分布,而后采用蚂蚁算法找出问题的最优解或近似最优解.通过具体算例,从数值计算上探索了遗传算法和蚂蚁算法融合后的优化能力,获得了满意的效果.