A Framework for Enhancing the Operational Phase of Traffic Management Plans

Road traffic emergencies are dangerous and unexpected situations that require immediate actions by the authorities. These actions involve to attend to the people who have been affected by the emergency and to minimize its consequences. A Traffic Management Plan (TMP) is a set of pre-defined measures and actions designed to produce an effective and efficient use of available resources in order to deal with a specific road incident. The operational phase of a TMP involves the coordination of several independent agencies (road managers, traffic police, firemen, etc.). These agencies must provide the resources required by the TMP in the deployment of the measures and actions. In this paper, a new framework to support the TMP operational phase is presented. This framework models each agency as an intelligent agent and it uses a reverse combinatorial distributed auction as the core component of a negotiation process. The goal of this negotiation process is to obtain a common agreement on the best possible allocation of resources taking into account the role, competencies and interest of the involved agencies. The framework has been implemented in a real scenario with real data. The tests developed have demonstrated that the system is able to manage the resources in terms of the execution time and the quality of the provided solutions

Apuntes de Teoría de Autómatas y Lenguajes Formales

IS17: Teoria d'Autòmats i Llenguatges Formal

Definición de una arquitectura para la asistencia en el diseño de productos

Ponencia presentada en el VI Congreso Internacional de Ingeniería de Proyectos celebrada en Barcelona en el año 2002This paper reports an ongoing research project aimed to develop a new Knowledge Based Engineering (KBE) system to support product design. Current knowledge modelling capabilities of KBE are considered and enhancements are brought by the adoption of recent advances of artificial intelligence in design and research in design process theory. The system takes the shape of a multi-agent architecture thus enabling modelling of organisational aspects of design activities.Se presenta un proyecto de investigación cuyo objetivo principal es desarrollar un nuevo sistema basado en el conocimiento (KBE) como ayuda al diseño de productos. El sistema considera las posibilidades actuales de modelado del conocimiento e incluye como innovaciones la adopción de los avances más recientes de la inteligencia artificial en el diseño e investigaciones propias sobre la teoría del proceso de diseño. El sistema se implementará en una arquitectura multiagente para permitir el modelado del conocimiento de la organización para ser aplicado en las actividades de diseño

Convergence approach in experimental results

Ponencia presentada en el IX Congreso Internacional de Ingeniería de Proyectos celebrado en Málaga en el año 2005The process of synthesis is based on phases of divergence and convergence. Many authors illustrate the divergence process to find better alternatives for the design requirements. Although there are still few explanations to prevent the possible combinatorial explosion present in complex systems. The heuristic state-space approach is an estimative process that can manage the complexity in the functional reasoning process (convergence). Its use differs from that of algorithms (mathematical procedures) because it is based on commonsense general rules taken from experience. Heuristic programs are well known for their capacity for self-learning, which can generate better optimised and more efficient solutions for design requirements. This article describes a research project carried out by the Engineering Design Group of Castellón on the generation of a better solution to a real design case, through the use of the best-first search algorithm presented by Zhang. We will therefore provide another point of view on the creation of a more efficient computational framework for the automated design process.El proceso de síntesis está basado en fases de divergencia y convergencia. Diversos autores demuestran el proceso de divergencia en la forma de búsqueda para la obtención de mejores alternativas para los requerimientos de diseño. Todavía existen muy pocas manifestaciones en el proceso de convergencia para evitar las posibles explosiones combinatorias presentes en sistemas más complejos. La aproximación heurística es un proceso que puede gestionar esa complejidad en el proceso de resolución funcional (convergencia). Su uso difiere de los algoritmos (procedimientos matemáticos) por estar basada en reglas generales sacadas de las experiencias. Los programas heurísticos son conocidos por su proceso de auto-aprendizaje, lo que puede generar soluciones más optimizadas y eficientes para los requerimientos de los procesos de diseño. Este artículo expone una investigación del Grupo de Ingeniería del Diseño de Castellón en la generación de una mejor solución en un caso real de diseño, a través del uso del algoritmo de búsqueda best-first search, presentado por Zhang. Además de aportar otro punto de vista en la creación de un modelo computacional más eficiente y eficaz para el diseño automatizado

A game engine to make games as multi-agent systems

Video games are applications that present design patterns that resemble multi-agent systems. Game objects or actors are like autonomous agents that interact with each other to describe complex systems. The purpose of this work is to develop a game engine to build games as multi-agent systems. The actors or game engine agents have a set of properties and behaviour rules with the end to interact with the environment of the game. The behaviour definition is established through a formal semantic based on predicate logic. The proposed engine tries to fulfil the basic requirements of the multi-agent systems, by adjusting the characteristics of the system, without affecting its potential. Finally, a set of games are introduced to validate the operation and possibilities of the engine

A multi-agent system for managing adverse weather situations on the road network

The development of traffic management and control strategies to improve traffic flows and road safety is necessary due to the high dynamism of traffic flows. The use of distributed intelligent systems can help the traffic organizations and the road operators to cope with possible incidents on the road network, especially when the incidents are related to adverse meteorological conditions. In that case, the probability of road accidents is increased due to the difficulty of driving under bad weather conditions. So, if the operators detect any meteorological incident, they must decide how to deal with it in order to improve traffic safety. In this paper we introduce a new multiagent system (MAS) to support traffic management when there appear meteorological problems in the road network. MAS technology helps to deal with the specific characteristics of traffic domain. The proposed MAS is able to work in two ways: a) coordinately, where all the agents work to solve weather problems in large networks and b) locally, where due to communications breakdown small groups of agents work together to inform road users about weather problems. The MAS has a rule-based system to deal with the meteorological data and decide the actions to take in front of any meteorological issue. This expert system also controls the quality of the data, improving the road operator confidence in the decisions taken by the expert system. However, weather sensors can provide wrong data, due to several factors (hardware failure, climate factors, etc.) so the rule based system controls these provided data by applying specific coherence and correlation rules to improve the quality of the taken decisions

Complete Integration of Team Project-Based Learning Into a Database Syllabus

Team project-based learning (TPBL) combines two learning techniques: 1) project-based learning (PBL) and 2) teamwork. This combination leverages the learning outcomes of both methods and places students in a real work situation where they must develop and solve a real project while working as a team. TPBL has been used in two advanced database subjects in Jaume I University (UJI)’s Computer Science degree program. This learning method was used for four years (academic years from 2018/2019 to 2021/2022) with positive outcomes. This study presents the project development, which includes teamwork formation, activities, timetable, and exercised learning competencies (both soft and specific). Further, the project’s results were evaluated from three different perspectives: 1) teamwork evaluation by teammates; 2) students’ opinions on the subject and project; and 3) subject final grades

Diseño e Implementación de una Arquitectura Multiagente para la Ayuda a la Toma de Decisiones en un Sistema de Control de Tráfico Urbano

El control de Tráfico Urbano es altamento complejo y dinámico: depende de la cantidad, tipo y calidad de los datos recibidos, de las previsiones de datos a recibir, del comportamiento del tráfico (actual, pasado y en previsión), el conocimiento del control a aplicar y del entorno de actuación. En esta tesis doctoral se analizan las diferentes aproximaciones realizadas desde el ámbito de la Inteligencia Artificial para identificar las principales debilidades de estos sistemas. Como resultado de este análisis se propone una nueva aproximación basada en la integración de datos procedentes de distintos sensores (mediante la adopción de un modelo cualitativo de datos), el análisis en tiempo real de la situación del tráfico (mediante un simulador macroscópico cualitativo basado en la densidad de vehículos en cola a la entrada de las intersecciones), la identificación de problemas actuales y potenciales de tráfico (mediante un análisis temporal cualitativo de la evolución de la densidad de vehículos) y la sugerencia de ejecución de acciones de control de tiempos de rojo para reducir las congestiones de tráfico actuales, evitar que evolucionen a situaciones de colapso y evitar congestiones futuras. Se define y diseña un prototipo con arquitectura multiagente que integra las características mencionadas. Su implementación se realiza en un sistema distribuido COTS. La ejecución del prototipo en pruebas de laboratorio (con datos reales de la ciudad de Castellón de la Plana) proporciona resultados que avalan la aproximación realizada

Diseño e Implementación de una Arquitectura Multiagente para la Ayuda a la Toma de Decisiones en un Sistema de Control de Tráfico Urbano

El control de Tráfico Urbano es altamento complejo y dinámico: depende de la cantidad, tipo y calidad de los datos recibidos, de las previsiones de datos a recibir, del comportamiento del tráfico (actual, pasado y en previsión), el conocimiento del control a aplicar y del entorno de actuación. En esta tesis doctoral se analizan las diferentes aproximaciones realizadas desde el ámbito de la Inteligencia Artificial para identificar las principales debilidades de estos sistemas. Como resultado de este análisis se propone una nueva aproximación basada en la integración de datos procedentes de distintos sensores (mediante la adopción de un modelo cualitativo de datos), el análisis en tiempo real de la situación del tráfico (mediante un simulador macroscópico cualitativo basado en la densidad de vehículos en cola a la entrada de las intersecciones), la identificación de problemas actuales y potenciales de tráfico (mediante un análisis temporal cualitativo de la evolución de la densidad de vehículos) y la sugerencia de ejecución de acciones de control de tiempos de rojo para reducir las congestiones de tráfico actuales, evitar que evolucionen a situaciones de colapso y evitar congestiones futuras. Se define y diseña un prototipo con arquitectura multiagente que integra las características mencionadas. Su implementación se realiza en un sistema distribuido COTS. La ejecución del prototipo en pruebas de laboratorio (con datos reales de la ciudad de Castellón de la Plana) proporciona resultados que avalan la aproximación realizada

A Smart Peri-Urban I2V Architecture for Dynamic Rerouting

Traffic congestion increases CO2 emissions, fuel consumption and travel times. These increases are related to the evolution of the growth in urban areas. Urban areas are being transforming into smart cities, that is, they use massive Infrastructure and Communication Technologies (ICT) to improve all the services with which citizens are provided. One objective of smart cities is to reduce traffic congestion. One approach to reaching this complex objective is by using Intelligent Transport Systems (ITS) to reroute vehicles in cities or in their periurban areas. Although, rerouting is a complex task, ICT have evolved a lot and they are currently being applied to the development of more powerful ITS. New ITS cooperative systems are commonly denoted as combinations of X2X (with X = I and/or X = V). In this paper, a new smart system based on I2V communications for peri-urban areas is described and implemented with the overall goal of improving rerouting, which implies reducing congestion. A demonstrator has been developed to evaluate the architecture and the impact that the use of public and up-to-date traffic information can have on rerouting. A real model of the peri-urban area of Castellon de la Plana has been used to evaluate the architecture. Using this model, several tests have been performed. Results show that traffic status information is valuable, but its impact on rerouting depends on the number of drivers that use this information appropriately