A column generation algorithm for the estimation of origin–destination matrices in congested traffic networks

The general problem of estimating origin–destination (O–D) matrices in congested traffic networks is formulated as a mathematical programme with equilibrium constraints, referred to as the demand adjustment problem (DAP). This approach integrates the O–D matrix estimation and the network equilibrium assignment into one process. In this paper, a column generation algorithm for the DAP is presented. This algorithm iteratively solves a deterministic user equilibrium model for a given O–D matrix and a DAP restricted to the previously generated paths, whose solution generates a new O–D trip matrix estimation. The restricted DAP is formulated via a single level optimization problem. The convergence on local minimum of the proposed algorithm requires only the continuity of the link travel cost functions and the gauges used in the definition of the DAP.     

多用户类多准则交通分配的势博弈与拥挤定价

交通管理者在解决路网拥挤问题时,并不知道出行者的出行效用,同时管理者难以对出行者的路径选择行为做出准确的观测.运用势博弈理论分析多用户类多准则交通行为的演化过程,得到了固定需求和弹性需求情形下的可容许动态(一种刻画出行者通过转换路径增加当前效用的近似调整行为的演化动态),证明当路段时间函数和逆需求函数为严格单调、连续、可微时,所对应的交通分配是势博弈问题的惟一Nash均衡点.进一步研究了固定需求下的可变拥挤道路收费问题,得到了在当前系统状态下实现系统最优交通分配的拥挤收费水平.     

Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach

This paper presents a dynamic distribution and assignment simulation model based on discrete time simulation techniques and dynamic route assignment for planning, engineering design, and operation analysis of big exhibition events from a pedestrian circulation perspective. Both, the distribution and assignment stages are incorporated in an interlaced way with a dynamic behavior along a specific time horizon. In the proposed model, the individual route choice is dynamically determined as consequence of facilities attractiveness and network congestion. Therefore, in contrast with other simulation approaches, it does not require the usual origin–destination trip matrices to describe the transportation demand or the specification of different paths to be followed by visitors. This modeling approach turns out to be very appropriate for the simulation of these big exhibition events where each visitor usually has multiple and a priori unordered destination choices after entering the scenario.     

多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

Incorporating transfer reliability into equilibrium analysis of railway passenger flow

Passenger’s transfer route choice behavior is one of the prominent research topics in the field of railway transportation. Existing traffic assignment approaches do not properly account for passenger’s expectation for transfer reliability. In this study, the transfer reliability is explicitly defined and a multi-class user equilibrium model is established, given which passengers choose the minimal-cost path based on their expected reliability thresholds. In particular, a path-based traffic assignment algorithm which combines a k-shortest path algorithm and the method of successive averages is proposed. The validity of the proposed approach is verified by an illustrative example. Using the proposed modeling approach, it is possible to determine the passenger’s collective route choice behavior based on the user equilibrium pattern. Moreover, the railway timetables can be evaluated and optimized based on the cost-based level of service estimation.     

Alternative formulations of a combined trip generation,trip distribution,modal split,and trip assignment model

The traditional four-step model has been widely used in travel demand forecasting by considering trip generation, trip distribution, modal split and traffic assignment sequentially in a fixed order. However, this sequential approach suffers from the inconsistency among the level-of-service and flow values in each step of the procedure. In the last two decades, this problem has been addressed by many researchers who have sought to develop combined (or integrated) models that can consider travelers’ choice on different stages simultaneously and give consistent results. In this paper, alternative formulations, including mathematical programming (MP) formulation and variational inequality (VI) formulations, are provided for a combined travel demand model that integrates trip generation, trip distribution, modal split, and traffic assignment using the random utility theory framework. Thus, the proposed alternative formulations not only allow a systematic and consistent treatment of travel choice over different dimensions but also have behavioral richness. Qualitative properties of the formulations are also given to ensure the existence and uniqueness of the solution. Particularly, the model is analyzed for a special but useful case where the probabilistic travel choices are assumed to be a hierarchical logit model. Furthermore, a self-adaptive Goldstein–Levitin–Polyak (GLP) projection algorithm is adopted for solving this special case.     

An equilibrium model for urban transit assignment based on game theory

The urban public transport system is portrayed as a special commodity market where passenger is consumer, transit operator is producer and the special goods is the service for passenger’s trip. The generalized Nash equilibrium game is applied to describe how passengers adjust their route choices and trip modes. We present a market equilibrium model for urban public transport system as a series of mathematical programmings and equations, which is to describe both the competitions among different transit operators and the interactive influences among passengers. The proposed model can simultaneously predict how passengers choose their optimal routes and trip modes. An algorithm is designed to obtain the equilibrium solution. Finally, a simple numerical example is given and some conclusions are drawn.     

A comparative study of modeling and solution approaches for the coordinated lot-size problem with dynamic demand

In the coordinated lot-size problem, a major setup cost is incurred when at least one member of a product family is produced and a minor setup cost for each different item produced. This research consolidates the various modeling and algorithmic approaches reported in the literature for the coordinated replenishment problem with deterministic dynamic demand. For the two most effective approaches, we conducted extensive computational experiments investigating the quality of the lower bound associated with the model’s linear programming relaxation and the computational efficiency of the algorithmic approaches when used to find heuristic and optimal solutions. Our findings indicate the superiority of the plant location type problem formulation over the traditional approach that views the problem as multiple single-item Wagner and Whitin problems that are coupled by major setup costs. Broader implications of the research suggest that other classes of deterministic dynamic demand lot-size problems may also be more effectively modeled and solved by adapting plant location type models and algorithms.     

Parking Capacity and Pricing in Park'n Ride Trips: A Continuous Equilibrium Network Design Problem

In this paper we consider the problem of designing parking facilities for park'n ride trips. We present a new continuous equilibrium network design problem to decide the capacity and fare of these parking lots at a tactical level. We assume that the parking facilities have already been located and other topological decisions have already been taken.The modeling approach proposed is mathematical programming with equilibrium constraints. In the outer optimization problem, a central Authority evaluates the performance of the transport network for each network design decision. In the inner problem a multimodal traffic assignment with combined modes, formulated as a variational inequality problem, generates the share demand for modes of transportation, and for parking facilities as a function of the design variables of the parking lots. The objective is to make optimal parking investment and pricing decisions in order to minimize the total travel cost in a subnetwork of the multimodal transportation system.We present a new development in model formulation based on the use of generalized parking link cost as a design variable.The bilevel model is solved by a simulated annealing algorithm applied to the continuous and non-negative design decision variables. Numerical tests are reported in order to illustrate the use of the model, and the ability of the approach to solve applications of moderate size.     

Chaos in a dynamic model of urban transportation network flow based on user equilibrium states

In this study, we investigate the dynamical behavior of network traffic flow. We first build a two-stage mathematical model to analyze the complex behavior of network flow, a dynamical model, which is based on the dynamical gravity model proposed by Dendrinos and Sonis [Dendrinos DS, Sonis M. Chaos and social-spatial dynamic. Berlin: Springer-Verlag; 1990] is used to estimate the number of trips. Considering the fact that the Origin–Destination (O–D) trip cost in the traffic network is hard to express as a functional form, in the second stage, the user equilibrium network assignment model was used to estimate the trip cost, which is the minimum cost of used path when user equilibrium (UE) conditions are satisfied. It is important to use UE to estimate the O–D cost, since a connection is built among link flow, path flow, and O–D flow. The dynamical model describes the variations of O–D flows over discrete time periods, such as each day and each week. It is shown that even in a system with dimensions equal to two, chaos phenomenon still exists. A “Chaos Propagation” phenomenon is found in the given model.     

A simplicial decomposition method for the transit equilibrium assignment problem

A transit equilibrium assignment problem assigns the passenger flows on to a congested transit (public transportation) network with asymmetric cost functions and a fixed origin-destination matrix. This problem which may be formulated in the space of hyperpath flows, is transformed into an equivalent problem in the space of total arc flows and an auxiliary variable. A simplicial decomposition algorithm is developed and its convergence is proved under the usual assumptions on the cost functions. The algorithm requires relatively little memory and its efficiency is demonstrated with computational results.     

相异成本和广义非线性需求下多寡头古诺模型及应用

在已有的寡头古诺模型基础上进一步扩展,建立了综合考虑竞争者成本差异和市场广义非线性需求的扩展多寡头古诺模型。给出了扩展多寡头古诺模型Nash均衡解的一般求解方法以及两类特殊需求函数下Nash均衡解的解析表达式。接着进一步探讨了模型的性质,重点讨论成本差异对均衡的影响及其与经典模型的异同,并利用模型的结论简单解释若干经济现象。模型性质探讨表明,边际成本差异会对均衡产量及其在各类寡头之间的分配产生重要影响。作为应用,将相关结论应用到以稀土开采管理为代表的稀有资源管理的策略分析上,提出了若干建议。     

Modelling dynamic network loading ontransportation networks through a continuous

The efforts spent by researchers in the last few years in traffic modelling have been focused on the modelization of dynamic behaviour of the several components making up a transportation system.In the field of traffic assignment, a large number of models and procedures have been proposed in order to perform Dynamic Network Loading (DNL), that is the reproduction of within-day variable link performances once a corresponding Origin/Destination (O/D) demand and users' choice model has been given. These models can be used both to evaluate traffic flows and, what is more relevant, to simulate the effects of regulation strategies on users' behaviour.

In this paper, after a brief review of the state of the art in this field, a continuous dynamic network loading model is proposed; it removes some of the drawbacks of other packet approach models proposed in literature and explicitly allows the en-route modification of the followed path. An algorithmic development of the model and a set of applications on text networks are also proposed.     

A tâtonnement process with fading memory,stabilization and optimal speed of convergence

The purpose of this work is to provide a way to improve stability and convergence rate of a price adjustment mechanism that converges to a Walrasian equilibrium. We focus on a discrete tâtonnement based on a two-agent, two-good exchange economy, and we introduce memory, assuming that the auctioneer adjusts prices not only using the current excess demand, but also making use of the past excess demand functions. In particular, we study the effect of computing a weighted average of the current and the previous excess demands (finite two level memory) and of all the previous excess demands (infinite memory). We show that suitable weights’ distributions have a stabilizing effect, so that the resulting price adjustment process converge toward the competitive equilibrium in a wider range of situations than the process without memory. Finally, we investigate the convergence speed toward the equilibrium of the proposed mechanisms. In particular, we show that using infinite memory with fading weights approaches the competitive equilibrium faster than with a distribution of quasi-uniform weights.     

Solving the Generalized Machine Assignment Problem in Group Technology

Many existing solution methodologies for machine assignment problems in group technology do not consider factors such as part demand, operation sequence and cost of intercellular moves. We formulate a 0-1 quadratic programming model that takes into account these factors in machine assignment. Two approaches are proposed to solve this problem. The first is an A*-based approach that generates optimal solutions. The second is a heuristic approach developed to solve problems with large number of machines and/or parts. The heuristic approach is shown to be efficient in producing good solutions in a computational study.     

A Stochastic Model for Combined Activity/Destination/Route Choice Problems

In travel behavior modeling, an important topic is to investigate what drives people to travel. A systematic analysis should examine why, where and when various activities are engaged in, and how activity engagement is related to the spatial and institutional organization of an urban area. In view of this, this paper presents a stochastic model for solving the combined activity/destination/route choice problem. It is a time-dependent model for long-term transport planning such as travel demand forecasting. The activity/destination choices are based on multinomial logit formulae and, the route choice is governed by stochastic user equilibrium principle. The solution algorithm is proposed together with a numerical example for demonstration. It is shown that the proposed modeling approach provides a powerful tool for fully understanding and predicting the complex travel behavior at strategic level. The work described in this paper was substantially supported by the grants from the National Natural Science Foundation of China (Project No. 79825101), the Chinese Academy of Sciences (MADIS Research Project) and the Research Grants Council of the Hong Kong Special Administrative Region (Project No. PolyU5077/97E).     

A Cournot–Nash–Bertrand game theory model of a service-oriented Internet with price and quality competition among network transport providers

This paper develops a game theory model of a service-oriented Internet in which profit-maximizing service providers provide substitutable (but not identical) services and compete with the quantities of services in a Cournot–Nash manner, whereas the network transport providers, which transport the services to the users at the demand markets, and are also profit-maximizers, compete with prices in Bertrand fashion and on quality. The consumers respond to the composition of service and network provision through the demand price functions, which are both quantity and quality dependent. We derive the governing equilibrium conditions of the integrated game and show that it satisfies a variational inequality problem. We then describe the underlying dynamics, and provide some qualitative properties, including stability analysis. The proposed algorithmic scheme tracks, in discrete-time, the dynamic evolution of the service volumes, quality levels, and the prices until an approximation of a stationary point (within the desired convergence tolerance) is achieved. Numerical examples demonstrate the modeling and computational framework.     

An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming

We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.     

运量分布与交通配流组合问题灵敏度分析方法

主要研究含单边约束的运量分布与交通配流组合问题的灵敏度分析计算方法.通过将该问题的数学规划模型等价转化为变分不等式模型,进而利用变分不等式问题的灵敏度分析方法,得到该组合问题中各决策变量关于扰动参数的导数公式.最后给出一个简单的数值算例说明该灵敏度分析方法的有效性.     

Quadratic cost flow and the conjugate gradient method

By introducing quadratic penalty terms, a convex non-separable quadratic network program can be reduced to an unconstrained optimization problem whose objective function is a piecewise quadratic and continuously differentiable function. A conjugate gradient method is applied to the reduced problem and its convergence is proved. The computation exploits the special network data structures originated from the network simplex method. This algorithmic framework allows direct extension to multicommodity cost flows. Some preliminary computational results are presented.