Formulation, existence, and computation of boundedly rational dynamic user equilibrium with fixed or endogenous user tolerance

This paper analyzes dynamic user equilibrium (DUE) that incorporates the notion of boundedly rational (BR) user behavior in the selection of departure times and routes. Intrinsically, the boundedly rational dynamic user equilibrium (BR-DUE) model we present assumes that travelers do not always seek the least costly route-and-departure-time choice. Rather, their perception of travel cost is affected by an indifference band describing travelers’ tolerance of the difference between their experienced travel costs and the minimum travel cost. An extension of the BR-DUE problem is the so-called variable tolerance dynamic user equilibrium (VT-BR-DUE) wherein endogenously determined tolerances may depend not only on paths, but also on the established path departure rates. This paper presents a unified approach for modeling both BR-DUE and VT-BR-DUE, which makes significant contributions to the model formulation, analysis of existence, solution characterization, and numerical computation of such problems. The VT-BR-DUE problem, together with the BR-DUE problem as a special case, is formulated as a variational inequality. We provide a very general existence result for VT-BR-DUE and BR-DUE that relies on assumptions weaker than those required for normal DUE models. Moreover, a characterization of the solution set is provided based on rigorous topological analysis. Finally, three computational algorithms with convergence results are proposed based on the VI and DVI formulations. Numerical studies are conducted to assess the proposed algorithms in terms of solution quality, convergence, and computational efficiency

Road network recovery from concurrent capacity-reducing incidents : model development and optimisation

Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions.Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions

Estrategias multi-mapa para el enrutamiento dinámico de tráfico urbano

La Directiva «Clean Transport, Urban Transport» de la Unión Europea identifica que la congestión en áreas urbanas tiene un coste anual acumulado de 100 billones de euros. El 60% de la población europea se ubica en áreas urbanas de más de 10,000 habitantes. De igual manera, se estima que la movilidad urbana es causante del 40% de emisiones de CO2 y hasta el 70% de otros contaminantes. Pero el problema es global y generalizado. La tesis aborda la problemática de optimizar tanto la planificación del tráfico urbano como su enrutamiento dinámico mediante una nueva técnica denominada Traffic Weighted Multi-Maps (TWM) con el fin de mitigar la congestión y sus efectos en los entornos urbanos. TWM propone la entrega selectiva de mapas de tráfico a los diferentes conjuntos de vehículos presentes en la red tenido en cuenta sus especificidades, el momento temporal, las situaciones de la via y el contexto. Para ello, recoge la colección de artículos científicos publicados en revistas indexadas respecto a TWM. La tesis analiza el uso de TWM para diversos casos de uso: mejora de la congestión en redes urbanas complejas mediante mapas de red aleatorizados, el encaminamiento selectivo de flotas, la reducción de la congestión ante incidentes aleatorios o planificados, y se plantean otros muchos escenarios. Asimismo, la tesis profundiza en cómo obtener distribuciones de mapas TWM óptimos para una cierta demanda de tráfico conocida por medio de datos históricos, proponiendo un conjunto de algoritmos de optimización basado en algoritmos evolutivos. El éxito de la implantación de un sistema de gestión inteligente de tráfico (ITS) depende de la adherencia de los conductores al mismo, dependiendo ésta de la percepción de la utilidad por los conductores. La tesis propone un modelo de experiencia de usuario-conductor para analizar el caso complejo de una red de tráfico que emplee diversos ITS de forma simultánea y no coordinada, con el objetivo de analizar la evolución en el tiempo de la adherencia de los conductores a TWM y así validar las hipótesis de partida respecto a su efectividad. La parte experimental de la tesis describe cómo se han empleado simulaciones de tráfico de diferente tipología: microscópicas y macroscópicas, desarrollando componentes de simulación específicos sobre plataformas abiertas de simulación de tráfico. Los resultados obtenidos son muy prometedores, obteniendo mejoras en la congestión global que oscilan entre el 20% y el 45%, con impacto en el resto de indicadores de emisiones y movilidad. Los estudios de simulación del comportamiento de los conductores en base a la utilidad percibida de TWM, muestran cómo la adherencia al mismo crece y se estabiliza garantizando el comportamiento global. Por último, se indican las posibles líneas futuras de investigación identificadas

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic