A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario

A mesoscopic traffic simulation based dynamic traffic assignment

XII Premio Abertis en gestión de infraestructuras de Transporte en la modalidad de tesis doctoral, 2014In terms of sustainability, traffic is currently a significant challenge for urban areas, where the pollution, congestion and accidents are negative externalities which have strongly impacted the health and economy of cities. The increasing use of private vehicles has further exacerbated these problems. In this context, the development of new strategies and policies for sustainable urban transport has made transport planning more relevant than ever before. Mathematical models have helped greatly in identifying solutions, as well as in enriching the process of making decisions and planning. In particular, dynamic network models provide a means for representing dynamic traffic behavior; in other words, they provide a temporally coherent means for measuring the interactions between travel decisions, traffic flows, travel time and travel cost. This thesis focuses on dynamic traffic assignment (DTA) models. DTA has been studied extensively for decades, but much more so in the last twenty years since the emergence of Intelligent Transport Systems (ITS). The objective of this research is to study and analyze the prospects for improving this problem. In an operational context, the objective of DTA models is to represent the evolution of traffic on a road network as conditions change. They seek to describe the assignment of the demand on different paths which connect every OD pair in a state of equilibrium. The behaviour following each individual decision during a trip is a time-dependent generalization of Wardrop's First Principle, the Dynamic User Equilibrium (DUE). This hypothesis is based on the following idea: When current travel times are equal and minimal for vehicles that depart within the same time interval , the dynamic traffic flow through the network is in a DUE state based on travel times for each OD pair at each instant of time ([ran-1996]). This work begins with the time-continuous variational inequalities model proposed by [friesz-1993] for solving the DUE problem. Different solutions can be used on the proposed DUE formulation. On the one hand, there are the so-called analytical approaches which use known mathematical optimization techniques for solving the problem directly. On the other hand, there are simulation-based formulations that approximate heuristic solutions at a reasonable computational cost. While analytical models concentrate mainly on deriving theoretical insights, simulation-based models focus on trying to build practical models for deployment in real networks. Thus, because the simulation-based formulation holds the most promise, we work on that approach in this thesis. In the field of simulation-based DTA models, significant progress has been made by many researchers in recent decades. Our simulation-based formulation separates the proposed iterative process into two main components: - A method for determining the new time dependent path flows by using the travel times on these paths experienced in the previous iteration. - A dynamic network loading (DNL) method, which determines how these paths flow propagate along the corresponding paths. However, it is important to note that not all computer implementations based on this algorithmic framework provide solutions that obtain DUE. Therefore, while we analyze both proposals in this thesis we focus on the preventive methods of flow reassignment because only those can guarantee DUE solutions. Our proposed simulation-based DTA method requires a DNL component that can reproduce different vehicle classes, traffic light controls and lane changes. Therefore, this thesis develops a new multilane multiclass mesoscopic simulation model with these characteristics, which is embedded into the proposed DUE framework. Finally, the developed mesoscopic simulation-based DTA approach is validated accordingly. The results obtained from the computational experiments demonstrate that the developed methods perform well.En los últimos tiempos, el problema del tráfi co urbano ha situado a las áreas metropolitanas en una difícil situación en cuanto a sostenibilidad se refi ere (en términos de la congestión, los accidentes y la contaminación). Este problema se ha visto acentuado por la creciente movilidad promovida por el aumento del uso del vehículo privado. Además, debido a que la mayor parte del trá fico es canalizada a través de los modos de carretera, el tiempo perdido por los usuarios al realizar sus viajes tiene un importante efecto económico sobre las ciudades. En este contexto, la plani cación de transporte se vuelve relevante a través del desarrollo de nuevas estrategias y políticas para conseguir un transporte urbano sostenible. Los modelos matemáticos son de gran ayuda ya que enriquecen las decisiones de los gestores de trá fico en el proceso de plani ficación. En particular podemos considerar los modelos de trá fico para la predicción, como por ejemplo los modelos de asignación dinámica de tráfi co (ADT), los cuales proveen de una representación temporal coherente de las interacciones entre elecciones de trá fico, fl ujos de trá fico y medidas de tiempo y coste. Esta tesis se centra en los modelos ADT. Durante las últimas décadas, los modelos ADT han sido intensamente estudiados. Este proceso se ha acelerado particularmente en los últimos veinte años debido a la aparición de los Sistemas Inteligentes de Transporte. El objetivo de esta investigación es estudiar y analizar diferentes posibilidades de mejorar la resolución del problema. En un contexto operacional, el objetivo de los modelos ADT es representar la evolución de la red urbana cuando las condiciones de trá fico cambian. Estos modelos tratan de describir la asignación de la demanda en los diferentes caminos que conectan los pares OD siguiendo un estado de equilibrio. En este caso se ha considerado que el comportamiento de los conductores en cada una de sus decisiones individuales tomadas durante el viaje es una generalización dependiente del tiempo del Primer Principio de Wardrop, denominada Equilibrio Dinámico de Usuario (EDU). Esta hipótesis se basa en la siguiente idea: para cada par OD para cada instante de tiempo, si los tiempos de viaje de todos los usuarios que han partido en ese intervalo de tiempo son iguales y mínimos, entonces el ujo dinámico de trá fico en la red se encuentra en un estado de EDU basado en los tiempos de viaje (Ran and Boyce (1996)). El presente trabajo toma como punto de partida el modelo de inecuaciones variacionales continuo en el tiempo propuesto por Friesz et al. (1993) para resolver el problema de equilibrio dinámico de usuario. Por un lado, se encuentran los denominados enfoques analíticos que utilizan técnicas matemáticas de optimización para resolver el problema directamente. Por otro lado, están los modelos cuyas formulaciones están basadas en simulación que aproximan soluciones heurísticas con un coste computacional razonable. Mientras que modelos analíticos se concentran principalmente en demostrar las propiedades teóricas, los modelos basados en simulación se centran en intentar construir modelos que sean prácticos para su utilización en redes reales. Así pues, debido a que las formulaciones basadas en simulación son las que se muestran más prometedoras a la práctica, en esta tesis se ha elegido este enfoque para tratar el problema ADT. En los últimos tiempos, el campo de los modelos ADT basados en simulación ha sido de especial interés. Nuestra formulación basada en simulación consiste en un proceso iterativo que consta de dos componentes principales, sistematizadas por Florian et al. (2001) como sigue: Un método para determinar los nuevos ujos (dependientes del tiempo) en los caminos utilizando los tiempos de viaje experimentados en esos caminos en la iteración previa. Un procedimiento de carga dinámica de la red (CDR) que determine cómo esos fl ujos se propagan a través de sus correspondientes caminos. Los algoritmos de reasignación de flujo pueden ser agrupados en dos categorías: preventivos y reactivos. Es importante notar aquí que no todas las implementaciones computacionales basadas en el marco algorítmico propuesto proporcionan una solución EDU. Por lo tanto, aunque en esta tesis analizamos ambas propuestas, nos centraremos en los métodos preventivos de reasignación de flujo porque son los que nos garantizan alcanzar la hipótesis considerada (EDU). Además, nuestro modelo ADT basado en simulación requiere de una componente de CDR que pueda reproducir diferentes clases de vehículos, controles semafóricos y cambios de carril. Así, uno de los objetivos de esta tesis es desarrollar un nuevo modelo de simulación de trá fico con dichas características (multiclase y multicarril), teniendo en cuenta que será una de las componentes principales del marco ADT propuesto.Award-winningPostprint (published version

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin

a cross-entropy based multiagent approach for multiclass activity chain modeling and simulation

This paper attempts to model complex destination-chain, departure time and route choices based on activity plan implementation and proposes an arc-based cross entropy method for solving approximately the dynamic user equilibrium in multiagent-based multiclass network context. A multiagent-based dynamic activity chain model is developed, combining travelers' day-to-day learning process in the presence of both traffic flow and activity supply dynamics. The learning process towards user equilibrium in multiagent systems is based on the framework of Bellman's principle of optimality, and iteratively solved by the cross entropy method. A numerical example is implemented to illustrate the performance of the proposed method on a multiclass queuing network.dynamic traffic assignment, cross entropy method, activity chain, multiagent, Bellman equation

Requirements for traffic assignment models for strategic transport planning: A critical assessment

Transport planning models are used all over the world to assist in the decision making regarding investments in infrastructure and transport services. Traffic assignment is one of the key components of transport models, which relate travel demand to infrastructure supply, by simulating (future) route choices and network conditions, resulting in traffic flows, congestion, travel times, and emissions. Cost benefit analyses rely on outcomes of such models, and since very large monetary investments are at stake, these outcomes should be as accurate and reliable as possible. However, the vast majority of strategic transport models still use traditional static traffic assignment procedures with travel time functions in which traffic flow can exceed capacity, delays are predicted in the wrong locations, and intersections are not properly handled. On the other hand, microscopic dynamic traffic simulation models can simulate traffic very realistically, but are not able to deal with very large networks and may not have the capability of providing robust results for scenario analysis. In this paper we discuss and identify the important characteristics of traffic assignment models for transport planning. We propose a modelling framework in which the traffic assignment model exhibits a good balance between traffic flow realism, robustness, consistency, accountability, and ease of use. Furthermore, case studies on several large networks of Dutch and Australian cities will be presented

A unified framework for traffic assignment: deriving static and quasi‐dynamic models consistent with general first order dynamic traffic assignment models

This paper presents a theoretical framework to derive static, quasi-dynamic, and semi-dynamic traffic assignment models from a general first order dynamic traffic assignment model. By explicit derivation from a dynamic model, the resulting models maintain maximum consistency with dynamic models. Further, the derivations can be done with any fundamental diagram, any turn flow restrictions, and deterministic or stochastic route choice. We demonstrate the framework by deriving static (quasidynamic) models that explicitly take queuing and spillback into account. These models are generalisations of models previously proposed in the literature. We further discuss all assumptions usually implicitly made in the traditional static traffic assignment model