Abstract

Proceedings of 11th International IFIP TC6 Conference, ONDM 2007, Athens, Greece, May 29-31, 2007. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-72731-6_46 Typically, core networks are provided with both optical and electronic physical layers. However, the interaction between the two layers is at present limited, since most of the traditional transport functionalities, such as traffic engineering, switching and restoration, are carried in the IP/MPLS layer. In this light, the research community has paid little attention to the potential benefits of the interaction between layers, multilayer capabilities, on attempts to improve the Quality of Service control. This work shows when to move incoming Label Switched Paths (LSPs) between layers based on a multilayer mechanism that trades off a QoS metric, such as end-to-end delay, and techno-economic aspects. Such mechanism follows the Bayesian decision theory, and is tested with a set of representative case scenarios. The authors would like to thank the support from the European Union VI Framework Programme e-Photon/ONe+ Network of Excellence (FP6-IST-027497). This work has also been partially funded by the IST Project NOBEL II (FP6-IST-027305). Document type: Part of book or chapter of book

Abstract

Proceedings of: 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010) Córdoba-Spain, June 04-06, 2010 This paper presents a new approach for trajectory segmentation in the area of Air Traffic Control, as a basic tool for offline validation with recorded opportunity traffic data. Our approach uses local information to classify each measurement individually, constructing the final segments over these classified samples as the final solution of the process. This local classification is based on a domain transformation using motion models to identify the deviations at a local scale, as an alternative to other global approaches based on combinatorial analysis over the trajectory segmentation domain. This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02. Publicado Document type: Part of book or chapter of book

Abstract

The SESAR (Single European Sky ATM Research) program is an ambitious re-search and development initiative to design the future European air traffic man-agement (ATM) system. The study of the behavior of ATM systems using agent-based modeling and simulation tools can help the development of new methods to improve their performance. This paper presents an overview of existing agent-based approaches in air transportation (paying special attention to the challenges that exist for the design of future ATM systems) and, subsequently, describes a new agent-based approach that we proposed in the CASSIOPEIA project, which was developed according to the goals of the SESAR program. In our approach, we use agent models for different ATM stakeholders, and, in contrast to previous work, our solution models new collaborative decision processes for flow traffic management, it uses an intermediate level of abstraction (useful for simulations at larger scales), and was designed to be a practical tool (open and reusable) for the development of different ATM studies. It was successfully applied in three stud-ies related to the design of future ATM systems in Europe. Document type: Part of book or chapter of book

Abstract

Heuristic methods are specially well suited to solve combinatorial problems. One of these problems is the distribution of petroleum products through oil pipelines networks. In this paper the problem is stated and solved using a multiobjective and constraint evolutionary optimization algorithm. Several objective functions has been defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some has been evaluated through a repairing function to avoid infeasible solutions. An example of working is given.

Abstract

This paper describes an agent-based approach for the simulation of air traffic management (ATM) in Europe that was designed to help analyze proposals for future ATM systems. This approach is able to represent new collaborative deci-sion processes for flow traffic management, it uses an intermediate level of ab-straction (useful for simulations at larger scales), and was designed to be a practi-cal tool (open and reusable) for the development of different ATM studies. It was successfully applied in three studies related to the design of future ATM systems in Europe.

Abstract

ATC is a critical area related with safety, requiring strict validation in real conditions (Kennedy & Gardner, 1998), being this a domain where the amount of data has gone under an exponential growth due to the increase in the number of passengers and flights. This has led to the need of automation processes in order to help the work of human operators (Wickens et al., 1998). These automation procedures can be basically divided into two different basic processes: the required online tracking of the aircraft (along with the decisions required according to this information) and the offline validation of that tracking process (which is usually separated into two sub-processes, segmentation (Guerrero & Garcia, 2008), covering the division of the initial data into a series of different segments, and reconstruction (Perez et al., 2006, Garcia et al., 2007), which covers the approximation with different models of the segments the trajectory was divided into). The reconstructed trajectories are used for the analysis and evaluation processes over the online tracking results. This validation assessment of ATC centers is done with recorded datasets (usually named opportunity traffic), used to reconstruct the necessary reference information. The reconstruction process transforms multi-sensor plots to a common coordinates frame and organizes data in trajectories of an individual aircraft. Then, for each trajectory, segments of different modes of flight (MOF) must be identified, each one corresponding to time intervals in which the aircraft is flying in a different type of motion. These segments are a valuable description of real data, providing information to analyze the behavior of target objects (where uniform motion flight and maneuvers are performed, magnitudes, durations, etc). The performance assessment of ATC multisensor/multitarget trackers require this reconstruction analysis based on available air data, in a domain usually named opportunity trajectory reconstruction (OTR), (Garcia et al., 2009). OTR consists in a batch process where all the available real data from all available sensors is used in order to obtain smoothed trajectories for all the individual aircrafts in the interest area. It requires accurate original-to-reconstructed trajectory’s measurements association, bias estimation and correction to align all sensor measures, and also adaptive multisensor smoothing to obtain the final interpolated trajectory. It should be pointed out that it is an off-line batch processing potentially quite different to the usual real time data fusion systems used for ATC, due to the differences in the data processing order and its specific 2 Document type: Book

Abstract

Current implementation of new technologies for Communication, Navigation, Surveillance and Air Traffic Management (CNS/ATM systems) along with computational improvements on airborne and ground systems developed in the last two decades, point out the need for more strategic navigation and air-traffic control procedures based on four-dimensional (position plus time) trajectories. Moreover, the CNS/ATM infrastructure will help to achieve more shared real-time information among aircraft, airlines and air-traffic services providers (i.e. Air Traffic Control –ATCproviders, meteorological information providers and air space resources information providers). Then, general requirements for a next-generation of Air Traffic Management (ATM) system is that entities must share real-time information about aircraft state and intentions, air-space state and resources, meteorological conditions and forecast, etc. From this information coordinated and strategic actions should be carried out by them in order to achieve efficient and free of conflict 4D trajectories.

Abstract

Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. Preliminary results show that ILP can be used to successfully learn to detect traffic problems.

Abstract

Segmentation is a tool presented for representation and approximation of data, according to a set of appropriate models. These procedures have applications to many different domains, such as time series analysis, polygonal approximation, Air Traffic Control,... Different heuristic and metaheuristic proposals have been introduced to deal with this issue. This thesis provides a novel multiobjective evolutionary method, analyzing the required general tools for the application evolutionary algorithms to real problems and the specific modifications required over the different steps of general proposals to adapt them to the segmentation domain. An introduction to the domain is presented by means of the design of a specific heuristic for segmentation of Air Traffic Control (ATC) data. This domain has a series of characteristics which make it difficult to be faced with traditional techniques: noisy data and a large number of measurements. The proposal works on two phases, using a pre-segmentation which introduces available domain information and applying a standard technique over this initial technique's results. Its results according to the presented domain, tested with a set of eight different representative trajectories, show competitive advantages compared to general approaches, which oversegmentate noisy data and, in some cases, exhibit poor scalability. This heuristic proposal shows the costly process of adapting available approaches and designing specific ones, along with the multi-objective nature of the problem, which requires the use of quality indicators for a proper comparison process. Applying evolutionary algorithms to segmentation provides several advantages, highlighting the fact that the problem dependance of heuristics make it costly to adapt these heuristics to new domains, as introduced by the designed heuristic to ATC. However, the practical application of these algorithms requires the study of a topic which has received little research effort from the community: stopping criteria. An evolutionary approach should contain a dynamic procedure which can determine when stagnation has taken place and stop the algorithm accordingly (as opposed to a-priori cost budgets, either in function evaluations or generations, which are usually applied for test datasets). Stopping criteria have been faced for single and multi-objective cases in this thesis. Single-objective stopping criteria have been approached proposing an active role of the stopping criteria, actively increasing the diversity in the variable space while tracking the updates in the fitness function. Thus, the algorithm reuses the information obtained for the stopping decision and feeds it to a stopping prevention mechanism in order to prevent problematic situations such as early convergence. The presented algorithm has been tested according to a set of 27 different functions, with different characteristics regarding their dimensionality, search space, local minima... The results show that the introduced mechanisms enhance the robustness of the results, due to the improved exploration and the early convergence prevention. Multi-objective stopping criteria are faced with the use of progress indicators (comparison measures of the quality of the evolution results at different generations) and an associated data gathering tool. The final proposal uses three different progress indicators, (hypervolume, epsilon and Mutual Dominance Rate) and considers them jointly according to a decision fusion architecture. The stagnation analysis is based on the least squares regression parameters of the indicators values, including a normality analysis as well. The online nature of these algorithms is highlighted, preventing the recomputation of the indicators values which were present in other available alternatives, and also focusing on the simplicity of the final proposal, in order to reduce the cost of introducing it into available algorithms. The proposal has been tested with instances of the DTLZ algorithm family, obtaining satisfactory stops with a standard set of configuration values for the technique. However, there is a lack of quantitative measures to determine the objective quality of a stop and to properly compare its value to other alternatives. The multi-objective nature of the segmentation problem is analyzed to propose a multiobjective evolutionary algorithm (MOEA) to deal with it. This nature is analyzed according to a selection of available approaches, highlighting the difficulties which had to be faced in the parameter configuration in order to guide the processes to the desired solution values. A multi-objective a-posteriori approach such as the one presented allows the decision maker to choose from the front of possible final solutions the one which suits him best, simplifying this process. The presented approach chooses SPEA2 as its underlying MOEA, analyzing different representation and initialization proposals. The results have been validated against a representative set of heuristic and metaheuristic techniques, using three widely extended curves from the polygonal approximation domain (chromosome, leaf and semicircle), obtaining statistically better results for almost all the different test cases. This initial MOEA approach had unresolved issues, such as the archiving technique complexity order, and also lacked the proper specific design considerations to adapt it to the application domain. These issues have been faced according to different improvements. First of all, an alternative representation is proposed, including partial fitness information and associated fitness-aware transformation operators (transformation operators which compute children fitness values according to their changes and the parents partial values). A novel archiving procedure is introduced according to the bi-objective nature of the domain, being one of them discrete. This leads to a relaxed Pareto dominance check, named epsilon glitches. Multi-objective local search versions of the traditional algorithms are proposed and tested for the initialization of the algorithm, along with the stopping criterion proposal which has also been adapted to the problem characteristics. The archive size in this case is big enough to contain all the different individuals in the optimal front, such that quality assessment is simplified and a simpler mechanism can be introduced to detect stagnation, according to the improvements in each of the possible individuals. The final evolutionary proposal is scalable, requires few configuration parameters and introduces an efficient dynamic stopping criterion. Its results have been tested against the original technique and the set of heuristic and metaheuristic techniques previously used, including the three original curves and also more complex versions of them (obtained with an introduced generation mechanism according to these original shapes). Even though the stopping results are very satisfactory, the obtained results are slightly worse than the original MOEA for the three simpler problem instances with the established configuration parameters (as was expected, due to the computational effort of the a-priori established number of generations and population size, based on the analysis of the algorithm's results). However, the comparison versus the alternative techniques stills shows the same statistically better results, and its reduced computational cost allows its application to a wider set of problems.

Abstract

Expert systems for decision support have recently been suc- cessfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational back- ground knowledge on the road network is essential. In this paper, we apply three state-of-the art ILP systems to learn how to detect traffic problems and compare their performance to the performance of a propositional learning system on the same problem.