On the dual approach to the traffic assignment problem

This paper attempts to explore the possibility of solving the traffic assignment problem with elastic demands by way of its dual problem. It is shown that the dual problem can be formulated as a nonsmooth convex optimization problem of which the objective function values and subgradients are conveniently calculated by solving shortest path problems associated with the transportation network. A subgradient algorithm to solve the dual problem is presented and limited computational experience is reported. The computational results are encouraging enough to demonstrate the effectiveness of the proposed approach.     

A discrete-convex programming approach to the simultaneous optimization of land use and transportation

Three design problems are discussed in this article. First, it is shown that the network design problem with congestion reduces to an all-or nothing traffic assignment problem under some assumptions on the congestion function and the investment cost function. Second, the land use design problem is formulated as an extension of the Koopmans-Beckmann problem and a heuristic is proposed to solve this problem. Third, it is shown that the seemingly more complex problem of designing jointly a land-use plan and a transportation network reduces to a pure land-use design problem. All that is needed to solve the joint optimization problem is a shortest path algorithm and a heuristic to solve the land use design problem. Computational experience is reported for each algorithm.     

On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem

This paper presents a study that characterizes, formulates, and solves the reverse logistic recycling flow equilibrium (RLRFE) problem. The RLRFE problem is concerned with the recycling channel in which recyclable collectors, processors, landfills, and demand markets form a multi-tiered network to process the recycled material flows from sources destined either for landfills or demand markets. Motivated by a government policy making or enterprise conglomerate recycling system design and operation needs, the RLRFE problem is elaborated from a system-optimal perspective using the variational inequality (VI) approach. For each origin–destination (OD) pair, the corresponding equilibrium conditions are established as a variation of the Wardrop second principle. In light of demand and cost function interactions, a nested diagonalization solution (ND) algorithm is proposed that gradually transforms the RLRFE problem into a traffic assignment model. To address multiple landfills in the recycling network and to understand how a variable-demand problem can be analyzed as a fixed-demand problem, we propose a supernetwork representation of the RLRFE problem. A numerical analysis on a test case illustrates the model formulation and the proposed algorithm.     

On the estimation of trip matrices in the case of missing and uncertain data

The problem of missing data in the base (calibration) year is of major importance when applying the gravity model in transportation planning, because base year data are used for calibration of the model. In this article, this problem is tackled by solving an optimization problem for the prediction year for values of the entropy level and the proportions of trips in a given stratification of the cells of the trip matrix estimated in the base year. The problem of uncertain data in the prediction year is handled by replacing equality constraints for the marginal totals in the prediction year by interval constraints. Computational results are given for trip data from the Linköping area.     

Approximate algorithms for the discrete network design problem

The discrete network design problem is one of finding a set of feasible actions (projects) from among a collection of possible actions, that when implemented, optimizes some objective function(s). This is a combinatorial optimization problem that is very expensive to solve exactly. This paper proposes two algorithms for obtaining approximate solutions to the discrete network design problem with much less computational effeort. The computational savings are achieved by approximating the original problem with a new formulation which is easier to solve. The first algorithm proposed solves this approximate problem exactly, while the second is even more efficient, but provides only a near-optimal solution to the approximate problem. Experience with test problems indicates that these approximations can reduce the computational effort by a factor of 3–5, with little loss in solution accuracy.     

An algorithm for the combined distribution and assignment problem

Much interest has recently been shown in the combination of the distribution and assignment models. In this paper we adopt a generalized Benders' decomposition to solve this combined problem for a system optimized assignment with linear link costs and explicit capacity constraints on link flows. The master problem which is generated is used to show that the combined problem can be viewed as a modified distribution problem, of gravity form, with a minimax instead of a linear objective function. An algorithm for solving the master problem is discussed, and some computational results presented.     

A quantum evolutionary algorithm for the second-best congestion pricing problem in urban traffic networks

This paper investigates the congestion pricing problem in urban traffic networks. A first-best strategy, a second-best strategy for toll leveling in closed cordons and a second-best strategy for determining both toll levels and toll points are considered. The problem is known to be a mixed integer programming model and formulated as a bi-level optimization problem, with an objective of maximizing the social welfare. A method is presented to solve the problem, based on a novel metaheuristic algorithm, namely quantum evolutionary algorithm (QEA). To verify the proposed method, the widely used genetic algorithm (GA) is also applied to solve the problem. The problem is solved for a medium-size urban traffic network and the results of the QEA are compared against the conventional GA. Computational results show that the QEA outperforms the GA in solution quality.     

Sustainability provisions in the bus-scheduling problem

The traditional vehicle scheduling problem attempts to minimize capital and operating costs. However, the carbon footprint and toxic air pollutants have become an increasingly important consideration. This paper studies the bus-scheduling problem and evaluates new types of buses that use alternative energy sources to reduce emissions, including some toxic air pollutants and carbon dioxide. A time-space network based approach is applied to formulate the problem to reduce the numbers of arcs in the underlying network; CPLEX is used to solve the problem. The results show that the bus-scheduling model can significantly reduce the bus emissions – hence reducing the carbon footprint of the transit operation – while only slightly increasing operating costs.     

Genetic algorithms approach to the problem of the automated vehicle identification equipment locations

The automated vehicle identification (AVI) equipment location problem entails determination of the best locations for the automated vehicle identification equipment. The paper attempts to solve the AVI equipment location problem as a multi‐objective optimization problem, thus determining the best locations on the basis of several criteria. The developed model is based on genetic algorithms. Testing of the model developed on the greater transportation networks is certainly one of the most important directions for the future research, as much as the development of models based on other metaheuristic approaches (Simulated Annealing, Taboo Search). The results obtained in this stage of the research are promising.     

Ranking the substantive problems in the Dutch Cost–Benefit Analysis practice

This paper investigates the perceptions of key participants in the Dutch Cost–Benefit Analysis (CBA) practice regarding substantive problems when appraising spatial-infrastructure projects with CBA. Two research methods were applied. Firstly, 86 key participants in the Dutch CBA practice were interviewed in-depth in order to obtain an overview and a ranking of perceived substantive problems with CBA in the Netherlands. Secondly, the people interviewed were also asked to fill in a written questionnaire in which they were asked to rank the substantive problems once again, in order to improve the validity of the ranking; 74 of the participants completed this questionnaire. The most important conclusions of this paper are, firstly, that key participants in the Dutch CBA practice consider ‘problems with the estimation of the non-monetized project effects’ as the most important substantive problem cluster and ‘problems with monetizing project effects’ as the second most important substantive problem cluster. Secondly, key participants in the Dutch CBA practice consider the ‘problem analysis’ in a CBA to be a very important substantive problem. Thirdly, there is, in a broad sense, consensus among the different groups in the Dutch CBA practice concerning their perception of the seriousness of problem clusters and the way they rank the problem clusters. Fourthly, a large part of the substantive problems mentioned by the key participants in the Dutch CBA practice are non-specific CBA problems.     

Ant colony optimization for the real-time train routing selection problem

This paper deals with the real-time problem of scheduling and routing trains in a railway network. In the related literature, this problem is usually solved starting from a subset of routing alternatives and computing the near-optimal solution of the simplified routing problem. We study how to select the best subset of routing alternatives for each train among all possible alternatives. The real-time train routing selection problem is formulated as an integer linear programming formulation and solved via an algorithm inspired by the ant colonies’ behavior. The real-time railway traffic management problem takes as input the best subset of routing alternatives and is solved as a mixed-integer linear program. The proposed methodology is tested on two practical case studies of the French railway infrastructure: the Lille terminal station area and the Rouen line. The computational experiments are based on several practical disturbed scenarios. Our methodology allows the improvement of the state of the art in terms of the minimization of train consecutive delays. The improvement is around 22% for the Rouen instances and around 56% for the Lille instances.     

绝热材料下沉对蒸汽管道热损失的影响

文中从绝热材料下沉的角度，利用形状因子法对高温蒸汽管线保温问题进行了探讨。通过理论分析和实验测试，得出了“复合硅酸盐保温涂料＋复合硅酸盐保温型材＋复合硅酸盐保温涂料”的保温结构，可有效改善绝热材料的下沉问题。     

On the convergence of iterative methods for the distribution balancing problem

A general distribution balancing problem, specified by the given outflows and inflows and the factorial form of its solution, is formulated. Solution uniqueness and boundedness is discussed—primarily in graph theoretical terms. An entropy maximisation problem, subject to general linear constraints, is transformed into an unconstrained optimisation problem by application of standard duality theory, and a relevant general convergence theorem for iterative solution methods is given. The optimum solution in a special case is identified with the flow solution. When expressed in flow variables, the dual objective has a unique and bounded optimum solution and is an appropriate unifying concept for measuring the rate of convergence of different solution methods. By regarding the balancing procedure as an iterative optimisation method, a new and simple proof of its convergence is given, together with some asymptotic results, which are also compared with those of Newton's method. It is pointed out that there are two different forms of Newton's method, according to the choice of variables—untransformed or transformed— in the original distribution balancing problem. When Newton's method is applied to the whole system of equations simultaneously, the trajectory of iterates is observed to depend on this variable choice. For the transformed variables it is noticed that the convergence with the balancing procedure is quicker than with Newton's method applied to the outflow- and inflow- equations, alternately. To guarantee global convergence with Newton's method and to increase the rate a supplementary linear search routine is recommended.     

Solving the bicriterion routing and scheduling problem for hazardous materials distribution

Hazardous materials routing and scheduling decisions involve the determination of the minimum cost and/or risk routes for servicing the demand of a given set of customers. This paper addresses the bicriterion routing and scheduling problem arising in hazardous materials distribution planning. Under the assumption that the cost and risk attributes of each arc of the underlying transportation network are time-dependent, the proposed routing and scheduling problem pertains to the determination of the non-dominated time-dependent paths for servicing a given and fixed sequence of customers (intermediate stops) within specified time windows. Due to the heavy computational burden for solving this bicriterion problem, an alternative algorithm is proposed that determines the k-shortest time-dependent paths. Moreover an algorithm is provided for solving the bicriterion problem. The proximity of the solutions of the k-shortest time-dependent path problem with the non-dominated solutions is assessed on a set of problems developed by the authors.     

Parameter estimability in the multinomial probit model

A particular parameter estimability problem in the multinomial probit model is considered. Making use of a model discussed in the literature, some problems with the usual method of specification are discussed. Some general comments are made on the problem of selecting a normalisation when using the multinomial probit model.     

A fuzzy stochastic approach to the multicriteria selection of an aircraft for regional chartering

This article deals with the problem of decision support for the selection of an aircraft. This is a problem faced by an airline company that is investing in regional charter flights in Brazil. The company belongs to an economic group whose core business is logistics. The problem has eight alternatives to be evaluated under 11 different criteria, whose measurements can be exact, stochastic, or fuzzy. The technique chosen for analyzing and then finding a solution to the problem is the multicriteria decision aiding method named NAIADE (Novel Approach to Imprecise Assessment and Decision Environments). The method used allows tackling the problems by working with quantitative as well as qualitative criteria under uncertainty and imprecision. Another considerable advantage of NAIADE over other multicriteria methods relies in its characteristics of not requiring a prior definition of the weights by the decision maker. As a conclusion, it can be said that the use of NAIADE provided for consistent results to that aircraft selection problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response

Pre-hospital Emergency Medical Service (EMS) provides the immediate and appropriate aid to patients in emergencies. As part of the traditional triad of first responders, EMS plays an important role in disaster response. In this work, the transportation infrastructure, which the EMS is dependent on, is considered. The objective of this research is to improve the effectiveness of EMS after the disaster by applying integer programming and the network-based partitioning to determine temporary locations for on-post EMS facilities. Integer Programming problems are formed for the optimization problem in different scales, and the Lagrangian Relaxation is adapted to extend the problem further into larger scale. Network based partitioning of demands are also proposed and tested. Numerical results are provided, and a case study is presented. In the case study, the facility location problem takes into consideration of both disaster triggered and usual EMS demand that forms a worst-case scenario. The analytical results are expected to facilitate decision making, and to serve as benchmarks for the planning of post-disaster EMS.     

Finding the k reliable shortest paths under travel time uncertainty

This paper investigates the problem of finding the K reliable shortest paths (KRSP) in stochastic networks under travel time uncertainty. The KRSP problem extends the classical K loopless shortest paths problem to the stochastic networks by explicitly considering travel time reliability. In this study, a deviation path approach is established for finding K α-reliable paths in stochastic networks. A deviation path algorithm is proposed to exactly solve the KRSP problem in large-scale networks. The A^* technique is introduced to further improve the KRSP finding performance. A case study using real traffic information is performed to validate the proposed algorithm. The results indicate that the proposed algorithm can determine KRSP under various travel time reliability values within reasonable computational times. The introduced A^* technique can significantly improve KRSP finding performance.     

Store-and-forward based methods for the signal control problem in large-scale congested urban road networks

The problem of designing network-wide traffic signal control strategies for large-scale congested urban road networks is considered. One known and two novel methodologies, all based on the store-and-forward modeling paradigm, are presented and compared. The known methodology is a linear multivariable feedback regulator derived through the formulation of a linear-quadratic optimal control problem. An alternative, novel methodology consists of an open-loop constrained quadratic optimal control problem, whose numerical solution is achieved via quadratic programming. Yet a different formulation leads to an open-loop constrained nonlinear optimal control problem, whose numerical solution is achieved by use of a feasible-direction algorithm. A preliminary simulation-based investigation of the signal control problem for a large-scale urban road network using these methodologies demonstrates the comparative efficiency and real-time feasibility of the developed signal control methods.     

Minimal-revenue congestion pricing Part II: An efficient algorithm for the general case

The second of a two-part series, this paper derives an efficient solution to the minimal-revenue tolls problem. As introduced in Part I, this problem can be defined as follows: Assuming each trip uses only a path whose generalized cost is smallest, find a set of arc tolls that simultaneously minimizes both average travel time and out-of-pocket cost. As a point of departure, this paper first re-solves the single-origin problem of Part I, modeling it as a linear program. Then with a change of variable, it transforms the LP's dual into a simple longest-path problem on an acyclic network. The multiple-origin problem – where one toll for each arc applies to all origins – solves analogously. In this case, however, the dual becomes an elementary linear multi-commodity max-cost flow problem with an easy bundling constraint and infinite arc capacities. After a minor reformulation that simplifies the model's input to better accommodate output from common traffic assignment software, a solution algorithm is exemplified with a numerical example.