交通分配的粒子群优化算法

为了方便合理地分配交通量,提出了交通量多路径分配的粒子群优化算法。算法的求解方法是在粒子群算法中构造了路径条数维的粒子空间,每维对应一条可行性路线,其值为对应路径所分配的交通量;对粒子进行归一化处理,使交通量守恒,并进行交通量的多路径分配;根据目标函数评价与筛选粒子,直到满足终止条件。实例计算结果表明:利用粒子群算法得到的目标函数值最小,各路段分配的交通量没有超容量现象,模型求解过程具有方向性,对交通分配的网络规模无限制,因此,粒子群优化算法可行、合理。     

航空公司机队集中调度的成本分析

机队集中调度能够提高机队调度的质量和效率,但目前缺乏评估机队调度质量的技术经济指标.本文提出飞机-航班节分配成本的概念作为其评估指标,分析该成本的三个组成:航班直接运行成本、航班溢出成本和罚成本,并根据航空运输协会工作组提出的成本理论得出航班直接运行成本,构造旅客溢出的概率模型,引入罚成本的相关系数.     

A new schedule-based transit assignment model with travel strategies and supply uncertainties

This paper proposes a new scheduled-based transit assignment model. Unlike other schedule-based models in the literature, we consider supply uncertainties and assume that users adopt strategies to travel from their origins to their destinations. We present an analytical formulation to ensure that on-board passengers continuing to the next stop have priority and waiting passengers are loaded on a first-come-first-serve basis. We propose an analytical model that captures the stochastic nature of the transit schedules and in-vehicle travel times due to road conditions, incidents, or adverse weather. We adopt a mean variance approach that can consider the covariance of travel time between links in a space–time graph but still lead to a robust transit network loading procedure when optimal strategies are adopted. The proposed model is formulated as a user equilibrium problem and solved by an MSA-type algorithm. Numerical results are reported to show the effects of supply uncertainties on the travel strategies and departure times of passengers.     

Optimization of transport investment and pricing policies: the role of transport pricing in network design

This paper investigates the role of transport pricing in network design and describes two facts about flow pattern in a transportation system. The first, illustrated by an example of Braess paradox, is that adding a new link to the network does not necessarily minimize the total travel time. The second is that introducing of appropriate toll pricing may reduce not only the total network time but also the travel time for each individual traveller. It follows with the investigations of different system objectives and different pricing policies (only toll pricing and distance‐based pricing are considered), and shows how they affect the system performance and flow pattern. Lastly, a systematic optimization process is proposed for integrated planning of transport network and pricing policies.     

圈层外推法在交通影响分析中的应用

针对目前交通影响分析实际项目缺乏路段阻抗矩阵这一现实情况,提出了一种基于路段饱和度的交通量分配方法--圈层外推法.由于路段饱和度既与阻抗之间存在显著的相关性,又较易通过观测获得,因此不仅具有很强的理论说服力,而且还具有广泛的实用价值.此外,结合数据结构知识,提出了基于VB的实现算法,并通过实例证明其正确性.     

基于道路设计与交通规划的道路选线优化模型

结合道路设计理论与交通规划理论,以地理信息系统为平台,开发道路选线优化模型。该模型是一个费用指向的优化问题,最小化费用目标函数中包括道路建设费用、土方工程费、道路交通诱发的环境污染的不经济费用,OD交通在路网上总走行时间的时间费用等。优化模型首先随机生成新建道路的空间位置候选方案集,并自动设计新建道路的平曲线和竖曲线,计算新建道路的各项工程费用。然后,对变化的路网进行自动拓扑,通过交通量分配得到OD交通在新路网上的走行时间和交通流特征,计算OD交通的环境负荷。最后,在遗传算法中判断候选方案的优劣,直到得到一个最佳的新建道路的空间位置方案为止。     

一类动态交通分配中的输出流模型及其数值解法

已知路段输入流,基于Greenshields提出的速度-密度关系模型以及Jayakrishnan et.al提出的改进的Greenshields 速度-密度关系模型所描述的路段交通流特征,分别给出了关于路段输出流的常微分方程模型.针对无法得到该模型的解析解,利用龙格-库塔-芬尔格算法给出初始条件下的数值解.在已知输出流的条件下,每个时刻的路段交通流的行程时间也相应给出.仿真结果表明,针对两类不同速度-密度关系所建立的输出流模型,所得到每个时刻的输出流基本相似,但路段行程时间存在明显差异.与交通流调查数据比较,基于改进的Greenshields速度-密度关系的输出流模型的行程时间更接近真实情况.     

随机交通分配中有效路径的定向树搜索算法

交通路网中有效路径的确定方法是进行各类随机交通流量分配的关键技术.文章将交通路网中节点位置的确定性与交通出行中路径选取的有向性相结合,模拟树的生长,提出了一种有效路径的定向树搜索算法.通过将该方法应用于随机用户均衡分配模型,说明了该方法的有效性.     

Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data

This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data.     

Multimodal,multiclass stochastic dynamic traffic assignment for evaluating information provision strategies

A multimodal, multiclass stochastic dynamic traffic assignment model was developed to evaluate pre‐trip and enroute travel information provision strategies. Three different information strategies were examined: user optimum [UO], system optimum [SO] and mixed optimum [MO]. These information provision strategies were analyzed based on the levels of traffic congestion and market penetration rate for the information equipment. Only two modes, bus and car, were used for evaluating and calculating the modal split ratio. Several scenarios were analyzed using day‐to‐day and within day dynamic models. From the results analyzed, it was found that when a traffic manager provides information for drivers using the UO strategy and drivers follow the provided information absolutely, the total travel time may increases over the case with no information. Such worsening occurs when drivers switch their routes and face traffic congestion on the alternative route. This phenomenon is the 'Braess Paradox'.