Abstract

Projet PRAXITELE; For a large urban area an alternative to public transportation and private cars is considered in the form of a "free" use of electric cars. Such "free" use of electric cars would require periodic redistribution of the cars among different dropoff/pickup stations by a fleet of finite capacity towtrucks stationed at the various depots on the road network. In this study we examine in detail the electric car redistribution problem. The redistribution activity is modelled as that of less than truck load, pickup and delivery with nonsimple paths, allowing for split pickups and deliveries. We propose a Mixed Integer Programming formulation of the problem and we test several solution which include constraint Programming, Lagrangian relaxation methodology applied to the MIP model, and an AI solution approach with a modified A* heuristic. The solution procedures here are designed to solve large practical instances of the car redistribution problem.

Abstract

In many applications, the main part of the computations may be encapsulated in compute-bounds kernels. Achieving high performance on compute-bound primitives at a low hardware cost has became an important challenge. OPAC was designed as the basic cell of a floating-point coprocessor dedicated to the execution of compute-bound kernels. Due to efficient hardware mechanisms for controlling and sequencing a pipeline performance close to a floating-point multiply-add per cycle per cell is reached on applications such as solving linear systems, FFTs or correlations in a microprocessor environment.

Abstract

This dissertation models and analyzes heterogeneous traffic flow, with a particular focus on mixed traffic flow consisting of cars and two-wheelers. The increase in traffic congestion induces commuters to switch to powered two wheelers (PTWs), i.e. motorcycle, mopeds and scooters, because of their high maneuverability and space efficiency. The growth in number of PTWs, combined with their unique mobility features, results in complex traffic characteristics which are difficult to recreate with the existing modeling approaches. We develop an analytical model that can accurately reproduce the traffic features in a mixed flow of cars and PTWs. The traffic stream is decomposed into two vehicle classes, PTWs and cars. The fundamental properties are derived by employing a porous flow approach. It is assumed that the speed of a vehicle class is dictated by the physical and motion properties of the vehicle class, and the distribution of free spaces on the road. We propose an approximation method to derive the free-space distribution. In order to explore broader aspects of the traffic flow characteristics, notably required by intelligent transport system (ITS) applications, we formulate the model in the Lagrangian and the Eulerian frameworks. Further, we provide a numerical method for the discretization of the mathematical model. We analyze the flow characteristics of mixed PTWs and cars traffic and identify important properties, which give insights for future ITS solutions and traffic policy makers. The applicability of the model for different ITS applications is illustrated. Finally, the developed model is validated using a microsimulation tool.; Cette dissertation modélise et analyse les flux de trafic hétérogènes, avec une attention particulière portée à la circulation de voitures et de deux-roues. L'augmentation du nombre de congestions de trafic a forcé les personnes désirant se déplacer à se diriger vers les le deux-roues (appelé ici PTWs = powered two wheelers), comme les motos, les mopeds et les scooters, du fait de leur facilité de manoeuvre et leur efficacité dans l'espace. L'augmentation du nombre de PTWs combinée au caractère unique de certaines de leurs fonctionnalités a résulté en un trafic complexe, donc les particularités sont difficiles à recréer avec les approches de modélisation existantes. Nous développerons ici un modele analytique permettant de reproduire de manière pertinente les particularités d'un flux de véhicules mêlant à la fois les voitures et les deux-roues. Le trafic se décompose en deux classes de véhicules : les PTWs et les voitures. Les propriétés fondamentales sont déduites en employant une approche "porous flow". On suppose que la vitesse d'un véhicule d’une certaine classe est dictée par les propriétés physiques et motrices du véhicule, ainsi que la distribution d'espace vide sur la route. Nous proposons une méthode d'approximation pour dériver la distribution d'espace vide. Dans le but d'explorer plus largement les caractéristiques du flux de trafic notamment requis par les applications de système de transport intelligent (ITS), nous formulons le modèle dans les cadres lagrangien et eulérien. Puis, nous feront appel à une méthode numérique pour la discrétisation du modèle mathématique. Se basant sur le modèle développé nous analyserons les caractéristiques du flux de trafic pour en identifier les propriétés les plus importantes qui nous permettrons de prédire de futures ITS applications et d'organisations du trafic. La possibilité d'appliquer le modèle pour les différentes ITS applications est illustrée par des exemples. Finalement, le modèle développé est validé à l'aide de l'outil de microsimulation.

Abstract

traffic flow management (ATFM) is a service established with the objective of contributing to safe, orderly, and expeditious flow of air traffic. The ATFM is divided into three phases. (1) The strategic phase takes place between 18 month and seven days prior to the day of operation and includes research, planning and coordination activities. The output of this phase is the capacity plan for the following year. (2) The pre-tactical phase is applied during 6 days prior to the days of operation and consists of planning and coordination activities. The output is the ATFM daily plan. (3) The tactical phase is applied on the day of the operation. This phase updates the daily plan according to the actual traffic and capacity. The management of the traffic is achieved through slot allocation.

Abstract

The stochastic nature of air traffic management arises mainly from uncertain operational events. This uncertainty may jeopardize the central flow management unit (CFMU) planning leading to safety problems and sub optimally used capacity. An absorption area is defined as one or several free slots in the planning so that the management of uncertainty is easier. Its aim is to compensate the aircraft uncertainty. The issue is to use the free slots in order to absorb uncertainty, and so not modify the initial planning. Finding the best configuration of the absorption areas corresponds to balancing optimally their size with the available capacity in order to absorb uncertainty and minimize "load loss" (unused capacity). This paper presents the initial results of slot allocation problem incorporating absorption areas: Under some elementary assumptions, the simulations show that, for whatever the rate of uncertainty is, the debit of traffic with absorption areas in slot allocation is always higher than without the absorption areas. A theoretical model is attempted to bring the proof to these empirical results.