Contribuições de aprendizado por reforço em escolha de rota e controle semafórico

A área de sistemas inteligentes de transporte há muito investiga como empregar tecnologias da informação e comunicação a fim de melhorar a eficiência do sistema como um todo. Isso se traduz basicamente em monitorar e gerenciar a oferta (rede viária, semáforos etc.) e a demanda (deslocamentos de pessoas e mercadorias). A esse esforço, mais recentemente, estão sendo adicionadas técnicas de inteligência artificial. Essa tem o potencial de melhorar a utilização da infraestrutura existente, a fim de melhor atender a demanda. Neste trabalho é fornecido um panorama focado especificamente em duas tarefas onde a inteligência artificial tem contribuições relevantes, a saber, controle semafórico e escolha de rotas. Os trabalhos aqui discutidos objetivam otimizar a oferta e/ou distribuir a demanda.The field of of intelligent transportation systems has long investigated how to employ information and communication technologies to improve the efficiency of the system as a whole. This basically means to monitor and manage both supply (traffic network, traffic signals etc.) and demand (vehicles, people and goods). More recently, artificial intelligence techniques are being added to this effort, as they have the potential to improve the usage of existing infrastructure to meet the corresponding demand. In this paper, an overview is given, focusing specifically on two tasks where artificial intelligence has made relevant contributions, namely, traffic signal controls and route choices. The works discussed here aim at optimize the supply and/or distribute the demand

Αναζήτηση θέσης στάθμευσης οχήματος με χρήση νοημοσύνης σμήνους

Η συγκεκριμένη έρευνα έγινε στα πλαίσια διπλωματικής εργασίας για την απόκτηση μεταπτυχιακού διπλώματος και περιλαμβάνει θεωρητικό κομμάτι αλλά και την ανάπτυξη αλγορίθμου που πραγματεύεται τη λύση του προβλήματος που μελετήθηκε καθώς και κώδικα για την υλοποίηση των προσομοιώσεων και των πειραμάτων. Αντικείμενο της εργασίας είναι η χρήση τεχνικών μηχανικής μάθησης / τεχνητής νοημοσύνης με σκοπό τη βελτιστοποίηση του χρόνου εύρεσης θέσης στάθμευσης στις σύγχρονες πόλεις και σε περιβάλλοντα τα οποία είναι σχεδόν κορεσμένα. Πιο συγκεκριμένα, εστιάζουμε στο κομμάτι της βελτιστοποίησης με τη βοήθεια της νοημοσύνης σμήνους και κυρίως μια παραλλαγή ενός ήδη υπάρχοντος αλγορίθμου. Η μέθοδος που προτείνεται είναι βασισμένη στον αλγόριθμο Ant Colony Optimization (ACO). Ο αλγόριθμος ACO είναι εμπνευσμένος από τη φύση και ειδικότερα από τις αποικίες των μυρμηγκιών. Το πρώτο σύστημα που υλοποίησε τον ACO εισήχθη από τον Marco Dorigo. Ο ACO είναι ένας μεθευριτικός αλγόριθμος γενικού σκοπού, ο οποίος μπορεί να χρησιμοποιηθεί για την επίλυση NP-complete προβλημάτων και είχε πολλές εξελίξεις. Ο αλγόριθμος ACO αποτελεί ιδανική λύση για δίκτυα που χαρακτηρίζονται από αυτονομία, λόγω της αυτοργάνωσης που παρουσιάζει. Κρατώντας τα στοιχεία της αυτοργάνωσης και της αυτονομίας του αρχικού αλγορίθμου στους πράκτορες του συστήματος, για τη λύση του προβλήματος της εύρεσης θέσης στάθμευσης σε εύλογο χρόνο χρησιμοποιήθηκε μια ανάστροφη παραλλαγή του ACO που από εδώ και πέρα θα ονομάζουμε βελτιστοποίηση ανεστραμμένης αποικίας μυρμηγκιών (IACO). Το σύστημα που προτείνεται αποτελείται από ισάξιους πράκτορες - οχήματα χωρίς ιδιαίτερη νοημοσύνη. Αποστολή του καθενός πράκτορα είναι η ταχύτερη εύρεση θέσης στάθμευσης σε μια περιοχή κοντινή του προορισμού κάθε οχήματος – πράκτορα. Έτσι φαίνεται πως δημιουργούνται σχέσεις ανταγωνισμού ανάμενα στους πράκτορες και όχι σχέσεις συνεργασίας, όπως για παράδειγμα στη φυσική περίπτωση μιας αποικίας μυρμηγκιών. Έτσι, ενώ οι φερομόνες που αφήνουν τα μυρμήγκια στα μονοπάτια τα οποία κινούνται είναι σήματα επικοινωνίας για ισχυροποίηση ενός μονοπατιού, στον αλγόριθμο που περιγράφουμε παρακάτω είναι σημάδια αποφυγής μια διαδρομής. Η αποφυγή επιλέγεται σε ένα μονοπάτι με μεγαλύτερη φερομόνη από ένα άλλο, αφού μια θέση στάθμευσης σε ένα τέτοιο μονοπάτι έχει πιο πολλές πιθανότητες να καλυφθεί από ανταγωνιστές πράκτορες που αναζητούν και αυτοί θέση στάθμευσης. Παρόλη τη σχέση ανταγωνισμού στο περιγραφόμενο σύστημα, φαίνεται πως ένας αλγόριθμος συνεργασίας των πρακτόρων ωφελεί το σύνολο και κατ’ επέκταση και τον εκάστοτε πράκτορα. Έτσι, με τη χρήση του συνεργατικού συστήματος νοημοσύνης σμήνους, από τα πειραματικά αποτελέσματα φαίνεται ότι μειώνεται ο χρόνος αναζήτησης θέσης στάθμευσης και κατ’ επέκταση η γενικότερη κίνηση σε τοπικό αλλά και γενικευμένο επίπεδο. Το προτεινόμενο σύστημα, όπως και τα περισσότερα συστήματα νοημοσύνης σμήνους, έχει εφαρμογή και καλή απόδοση σε μικρές αλλά και σε μεγαλύτερες κλίμακες μεγεθών κορεσμού, πρακτόρων αλλά και σημείων στάθμευσης.This work was carried out in the context of a diploma thesis for obtaining a master's degree and includes a theoretical part and the development of an algorithm that deals with the solution of the problem at hand, as well as code for simulations and experiments. The objective of the following work is the exploitation of machine learning / artificial intelligence techniques to optimize the time of finding a parking space in modern cities and in environments that are mostly saturated. More specifically, we focus on the part of optimization with the help of swarm intelligence and mainly a variation of an existing algorithm. The proposed method is based on the Ant Colony Optimization (ACO) algorithm. The ACO algorithm is inspired by nature, in particular by the colonies of ants. The first system to implement ACO was introduced by Marco Dorigo. ACO is a general-purpose metaheuristic algorithm that can be used to solve NP-complete problems and has had many developments. The ACO algorithm is an ideal solution for networks characterized by autonomy, due to its self-organization. Keeping the good part of the self-organization and autonomy of the original algorithm in the system agents, we propose a solution for the parking space problem in a reasonable time, with a reverse variant of ACO which from now on we will call inverted ant colony optimization (IACO). The proposed system consists of equivalent agents - vehicles without special intelligence. The mission of each agent is to find parking space the fastest in an area close to the destination of each vehicle - agent. Thus, it seems that competitive relations are created for the agents and not cooperative relations, as for example in the natural case of an ant colony. So, while the pheromones that ants leave on the paths they move are communication signals to reinforce a path, in the algorithm described below are signs of avoiding a path. Avoidance is chosen on a path with more pheromone than another, since a parking space on such a path is more likely to be covered by competing agents who are also looking for a parking space. Despite the competitive relationship in the described system, it seems that an algorithm of cooperation of the agents benefits the whole and, by extension, the respective agent

Análisis de la eficiencia de los sistemas de colonias de hormigas para problemas de accesibilidad

A pesar de los avances en materia de predicción, los desastres naturales siguen teniendo consecuencias devastadoras. Entre los principales problemas a los que se enfrentan los equipos de ayuda y rescate después de un desastre natural o provocado por el hombre se encuentra la planificación de las tareas de reparación de carreteras para conseguir la máxima ventaja de los limitados recursos económicos y humanos. En la presente Tesis Fin de Máster se intenta dar solución al problema de la accesibilidad, es decir, maximizar el número de supervivientes que consiguen alcanzar el centro regional más cercano en un tiempo mínimo mediante la planificación de qué carreteras rurales deberían ser reparadas dados unos recursos económicos y humanos limitados. Como se puede observar, es un problema combinatorio ya que el número de planes de reparación y conexiones entre las ciudades y los centros regionales crece de forma exponencial con el tamaño del problema. Para la resolución del problema se comienza analizando una adaptación básica de los sistemas de colonias de hormigas propuesta por otro autor y se proponen múltiples mejoras sobre la misma. Posteriormente, se propone una nueva adaptación más avanzada de los sistemas de colonias de hormiga al problema, el ACS con doble hormiga. Este sistema hace uso de dos tipos distintos de hormigas, la exploradora y la trabajadora, para resolver simultáneamente el problema de encontrar los caminos más rápidos desde cada ciudad a su centro regional más cercano (exploradora), y el de obtener el plan óptimo de reparación que maximice la accesibilidad de la red (trabajadora). El algoritmo propuesto se ilustra por medio de un ejemplo de gran tamaño que simula el desastre natural ocurrido en Haití, y su rendimiento es comparado con la combinación de dos metaheurísticas, GRASP y VNS.---ABSTRACT---In spite of the advances in forecasting, natural disaster continue to ocasionate devastating consequences. One of the main problems relief teams face after a natural or man-made disaster is how to plan rural road repair work to take maximum advantage of the limited available financial and human resources. In this Master´s Final Project we account for the accesability issue, that is, to maximize the number of survivors that reach the nearest regional center in a minimum time by planning whic rural roads should be repaired given the limited financial and human resources. This is a combinatorial problem since the number of possible repairing solutions and connections between cities and regional centers grows exponentially with the size of the problem. In order to solve the problem, we analyze the basic ant colony system adaptation proposed by another author and point out multiple improvements on it. Then, we propose a novel and more advance adaptation of the ant colony systems to the problem, the double- ant ACS. This system makes use of two diferent type of ants, the explorer and the worker, to simultaneously solve the problem of finding the shorthest paths from each city to their nearest regional center (explorer), and the problem of identifying the optimal repairing plan that maximize the network accesability (worker). The proposed algorithm is illustrated by means of a big size example that simulates the natural disaster occurred in Haiti, and its performance is compared with a combination of two metaheuristics, GRASP and VNS

Integrating Electric Buses in Conventional Public Transit: A First Appraisal

O caos urbano, a instabilidade dos preços de combustível e os efeitos cada vez mais evidentes da emissão de poluentes atmosféricos têm tornado o transporte individual privado frequentemente desagradável e dispendioso. Sistemas de transporte público são uma resposta possível para a redução do número de automóveis nas estradas; Em particular, autocarros são uma alternativa atraente, visto dependerem maioritariamente de infraestruturas pre-existentes sem necessitarem de mudanças significativas. Adicionalmente, o uso de autocarros elétricos na rede significaria uma maior redução de emissões poluentes e consumos de combustível mais baixos, quando comparado com frotas de autocarros exclusivamente convencionais.No entanto, veículos elétricos também apresentam desvantagens, tais como menor potência e autonomia, um escasso número de pontos de recarga em grande parte das redes urbanas e um desempenho altamente dependente das caraterísticas da via onde circulam. Isto poderá ser resolvido recorrendo a uma abordagem mais conservadora - frotas híbridas, constituídas por autocarros elétricos e convencionais.Nesta dissertação pretende-se abordar duas questões importantes neste tipo de frotas: como estimar o desempenho dos autocarros elétricos integrados na frota e como obter o equilíbrio ótimo entre os dois tipos de veículos. Para atingir estes objetivos, são analisados dados reais e simulados de uma rede de autocarros no Porto, Portugal, e são aplicadas abordagens heurísticas para obter a composição ideal das frotas híbridas.Este estudo, suportado por dados reais que cobrem uma grande parte da rede de autocarros, para além de sugerir configurações de frotas para a rede em consideração, também formula recomendações gerais para o planeamento e gestão de redes urbanas sustentáveis.Private individual transportation is becoming cumbersome and expensive, as urban traffic turns more chaotic, fuel prices increase and the effects of pollutant emissions become evident. Public transit systems are an answer to reducing the number of cars on the road. Particularly, buses are an attractive alternative, as they mostly depend on pre-existent infrastructure, having no need for complex changes. Making some of these buses electric would mean even less tailpipe emissions and cheaper consumption costs, when compared to fully conventional fleets.However, electric vehicles have disadvantages, such as lower power and autonomy, scarce recharge points on most urban networks and vehicle performance greatly dependent on route characteristics. We can solve this with a more conservative approach - using hybrid fleets, comprised by both electric and conventional buses.This dissertation intends on tackling two main aspects with this kind of fleets: estimating the performance of the integrated electric buses and obtaining optimal balances of both kinds of vehicles. To fulfil these goals, real and simulated data of a bus network in Porto, Portugal, is analysed and heuristic approaches are used to devise hybrid fleet arrangements.This study, supported by real data covering a large scope of the public transit network, in addition to suggesting fleet configurations for the specific network under consideration, also formulates general recommendations towards sustainable urban network planning and management