Determining highway corridors

In the highway development process, the first planning stage is that of selecting a corridor along which the highway is to pass. Highway corridor selection represents a multicriteria decision process in which a variety of social, enviromental and economic factors must be evaluated and weighted for a large number of corridor alternatives. This paper proposes a demand-based approach to provide a set of potential corridors. The problem is formulated as a continuous location model which seeks a set of optimal corridors with respect to the demand of potential users while satisfying budget constraints. This model uses geographical information in order to estimate the length-dependent costs (such as pavement and construction cost) and the cost of earth movement. A procedure for finding the best local minima of the optimization model is proposed. This method is tested using the Particle Swarm Optimization algorithm, two algorithms of the Simulated Annealing type and the Simplex Nedelmar method. An application using the Castilla-La Mancha\s geographic database is presented

A continuous bi-level model for the expansion of highway networks

Adding new corridors to a highway network represents a multicriteria decision process in which a variety of social, environmental and economic factors must be evaluated and weighted for a large number of corridor alternatives. This paper proposes a new bi-level continuous location model for expansion of a highway network by adding several highway corridors within a geographical region. The upper level problem determines the location of the highway corridors, taking into account the budgetary and technological restrictions, while the lower level problem models the users\\\\\\\\ behavior in the located transport network (choices of route and transport system). The proposed model takes into account the demand in the area served by the new network highway corridors, the available budget and the user behaviour. This model uses geographical information in order to estimate the length-dependent costs (such as pavement and construction costs) and the cost of earth movement. The proposed method is tested using the Standard Particle Swarm Optimization algorithm and applied to the Castilla-La Mancha geographic database. The previous methodology has been extended to a multiobjective approach in order to handling uncertainty in demand

A framework for derivative free algorithm hybridization

Column generation is a basic tool for the solution of largescale mathematical programming problems. We present a class of column generation algorithms in which the columns are generated by derivative free algorithms, like population-based algorithms. This class can be viewed as a framework to define hybridization of free derivative algorithms. This framework has been illustrated in this article using the Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms, combining them with the Nelder-Mead (NM) method. Finally a set of computational experiments has been carried out to illustrate the potential of this framework

A Methodology for the Automatic Regulation of Intersections in Real Time using Soft-computing Techniques

This work presents an application of diverse soft-computing techniques to the resolution of semaphoric regulation problems. First, clustering techniques are used to discover the prototypes which characterize the mobility patterns at an intersection. A prediction model is then constructed on the basis of the prototypes found. Fuzzy logic techniques are used to formally represent the prototypes in this prediction model and these prototypes are parametrically defined through frameworks. The use of these techniques supposes a substancial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computatio

A modeling framework for the estimation of optimal CO2 emission taxes for private transport

In this paper, a novel modeling framework is proposed for the estimation of optimal CO2 emission taxes for urban traffic. The framework is based on a bi-level model comprising a combined equilibrium model with elastic demand and a \\\\pollution taxes\\\\ (PTs) estimation model based on vehicle kilometers traveled and emissions produced. A bi-level optimization problem is proposed for the PT estimation model (PTM) in order to provide the minimum price which reduces emissions generated in an urban area to a desired value dependent on the environmental goals. To solve this problem, the Regula Falsi method is proposed and it exhibits a high enough rate of convergence. Two tests using the Nguyen and Dupuis network and Barcelona network (Spain) have been performed to test the convergence of our resolution method and the applicability of the proposal over networks with different sizes. The results are very promising and allow the implicit definition of the behavior of users against different PT prices

An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation