Academic literature on the topic 'Mesoscopic traffic simulation'

In 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.<br>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.

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.<br>Includes bibliographical references (p. 125-129).<br>by Kunal Kamlakar Kundé.<br>S.M.in Transportation

Evacuation processes can be evaluated using different simulation models. However, recently, microscopic simulation models have become a more popular tool for this purpose. The objectives of this study are to model multiple evacuation scenarios and to compare the INTEGRATION microscopic traffic simulation model against the MATSim mesoscopic model. Given that the demand was the same for both models, the comparison was achieved based on three indicators: estimated evacuation time, average trip duration, and average trip distance. The results show that the estimated evacuation times in both models are close to each other since the Origin-Destination input file has a long tail distribution and so the majority of the evacuation time is associated when travelers evacuate and not the actual evacuation times. However, the evaluation also shows a considerable difference between the two models in the average trip duration. The average trip duration using INTEGRATION increases with increasing traffic demand levels and decreasing roadway capacity. On the other hand, the average trip duration using MATSim decreases with increasing traffic demand and decreasing the roadway capacity. Finally, the average trip distance values were significantly different in both models. The conclusion showed that the INTEGRATION model is more realistic than the MATSim model for evacuation purposes. The study concludes that despite the large execution times of a microscopic traffic simulation, the use of microsimulation is a worthwhile investment.<br>Master of Science

The Multi-resolution Assignment and Loading of Transport Activities (MALTA) system is a simulation-based Dynamic Traffic Assignment model that exploits the advantages of multi-processor computing via the use of the Message Passing Interface (MPI) protocol. Spatially partitioned transportation networks are utilized to estimate travel time via alternate routes on mega-scale network models, while the concurrently run shortest path and assignment procedures evaluate traffic conditions and re-assign traffic in order to achieve traffic assignment goals such as User Optimal and/or System Optimal conditions.Performance gain is obtained via the spatial partitioning architecture that allows the simulation domains to distribute the work load based on a specially designed Recursive On-line Load Balance model (ROLB). The ROLB development describes how the transportation network is transformed into an ordered node network which serves as the basis for a minimum cost heuristic, solved using the shortest path, which solves a multi-objective NP Hard binary optimization problem. The approach to this problem contains a least-squares formulation that attempts to balance the computational load of each of the mSim domains as well as to minimize the inter-domain communication requirements. The model is developed from its formal formulation to the heuristic utilized to quickly solve the problem. As a component of the balancing model, a load forecasting technique is used, Fast Sim, to determine what the link loading of the future network in order to estimate average future link speeds enabling a good solution for the ROLB method.The runtime performance of the MALTA model is described in detail. It is shown how a 94% reduction in runtime was achieved with the Maricopa Association of Governments (MAG) network with the use of 33 CPUs. The runtime was reduced from over 60 minutes of runtime on one machine to less than 5 minutes on the 33 CPUs. The results also showed how the individual runtimes on each of the simulation domains could vary drastically with naïve partitioning methods as opposed to the balanced run-time using the ROLB method; confirming the need to have a load balancing technique for MALTA.

One of the most effective measures of congestion control on freeways has been ramp metering, where vehicle entry to the freeway is regulated by traffic signals (meters). Meters are run with calibrated influx rates to prevent highway saturation. However, recent observations of some metering sites in San Diego, CA indicate that metering, during peak hour demand, is helping freeway flow while sometimes creating considerable traffic back-ups on local streets, transferring congestion problems from the freeway to intersections. Metering problems stem largely from the difficulty of designing an integrated, dynamic metering scheme that responds not only to changing freeway conditions but also to fluctuating demand throughout the ramp network; a scheme whose objective is to maintain adequate freeway throughput as well as minimize disproportionate ramp delays and queue overspills onto surface streets. Simulation modeling is a versatile, convenient, relatively inexpensive and safe systems analysis tool for evaluating alternative strategies to achieve the above objective. The objective of this research was to establish a basic building block for a discrete system simulation model, ONRAMP, based on a stochastic, mesoscopic, queueing approach. ONRAMP is for modeling entrance ramp geometry, vehicular generation, platooning and arrivals, queueing activities, meters and metering rates. The architecture of ONRAMP's molecular unit is designed in a fashion so that it can be, with some model calibration, duplicated for a number of ramps and, if necessary, integrated into some other larger freeway network models. SLAM.II simulation language is used for computer implementation. ONRAMP has been developed and partly validated using data from eight ramps at Interstate-B in San Diego. From a systems perspective, simulation will be short-sided and problem analysis is incomplete unless the other non-technical metering problems are explored and considered. These problems include the impacts of signalizing entrance ramps on the vitality of adjacent intersections, land use and development, "fair" geographic distribution of meters and metering rates throughout the freeway corridor, public acceptance and enforcement, and the role and influence of organizations in charge of decision making in this regard. Therefore, an outline of a contextual systems approach for problem analysis is suggested. Benefits and problems of freeway control via ramp metering, both operational short-term and strategic long-term, are discussed in two dimensions: global (freeway) and local (intersection). The results of a pilot study which includes interviews with field experts and law enforcement officials and a small motorist survey are presented.

La pollution de l'air urbain, principalement causée par les émissions des véhicules, reste un problème crucial pour les grandes villes européennes qui s'efforcent de respecter les normes de qualité de l'air ambiant de l'Union européenne. Bien que des politiques environnementales plus strictes soient essentielles, leur mise en œuvre entraîne souvent des coûts économiques et sociaux importants. Par conséquent, des modèles et des simulations fiables sont indispensables pour évaluer l'efficacité de ces stratégies, garantissant qu'elles atteignent les réductions souhaitées des concentrations de polluants sans imposer de charges excessives à la société.Les approches traditionnelles pour estimer les émissions de polluants liés au trafic routier dans des scénarios prospectifs reposent sur des modèles de trafic et d'émissions à différentes échelles, chacun ayant des limites spécifiques en contexte urbain. Les modèles macroscopiques utilisent une approche agrégée qui manque de granularité pour capturer les pics d'émissions au sein du réseau. Les modèles microscopiques, bien qu'ils fournissent des analyses détaillées, sont limités par des exigences de données importantes et une complexité de calcul élevée.Cette thèse présente deux nouvelles méthodologies qui améliorent la modélisation des émissions à grande échelle grâce à l'intégration de l'apprentissage profond dans le flux de travail. La première méthodologie, SPG-M, comble le fossé entre les modèles de trafic mésoscopiques et les modèles d'émissions microscopiques, une combinaison auparavant jugée irréalisable en raison des problèmes de compatibilité des données. L'innovation clé réside dans un générateur de profils de vitesse basé sur l'apprentissage profond, entrainé sur des données de conduite réelles, qui transforme les résultats des modèles de trafic mésoscopiques en profils de vitesse instantanés nécessaires au fonctionnement des modèles d'émissions microscopiques. Cette intégration permet d'estimer les émissions de manière très détaillée au niveau local en prenant en compte toutes les variations de vitesse sur un tronçon, tout en fournissant des estimations globales précises.La deuxième méthodologie combine un modèle de trafic mésoscopique avec un modèle d'émissions mésoscopique basé sur l'apprentissage profond. Ce modèle d'émissions est entrainé sur des données d'émissions synthétiques générées par un modèle d'émissions microscopique. Cette méthodologie offre une précision comparable au SPG-M tout en améliorant considérablement l'efficacité du calcul.Les deux méthodologies ont été appliquées avec succès dans la région Île-de-France, qui compte plus de 12 millions d'habitants, pour prédire les émissions de CO2 et de NOx, démontrant leur capacité de mise à l'échelle et leur aptitude à maintenir une grande granularité dans les grandes zones urbaines. Le processus de validation a consisté à évaluer chaque composant des méthodologies proposées séparément. Enfin, les émissions ont été comparées aux modèles d'émissions macroscopiques bien établis, à savoir HBEFA et COPERT, ainsi qu'aux campagnes de mesure des émissions. Les résultats montrent que, bien que les émissions globales de CO2 soient comparables entre tous les modèles, les émissions de NOx sont sous-estimées par les modèles macroscopiques. Des écarts sont également observés au niveau local, les modèles macroscopiques ne parvenant pas à capturer les zones de fortes et faibles accélérations, responsables respectivement des zones de fortes et faibles émissions<br>Urban air pollution, predominantly caused by vehicular emissions, remains a critical issue for major European cities striving to meet European Union ambient air quality standards. While stricter environmental policies are essential, implementing them often incurs substantial economic and social costs. Therefore, reliable models and simulations are essential to evaluate the effectiveness of these strategies, ensuring they achieve the desired reductions in pollutant concentrations without imposing undue burdens on society.Traditional approaches to estimating road traffic pollutant emissions for prospective scenarios rely on traffic and emission models at various scales, each with distinct limitations in urban contexts. Macroscopic models employ an aggregated approach that lacks the granularity needed to capture emission peaks within the network. Microscopic models, while providing detailed analyses, are constrained by extensive data requirements and high computational complexity.This thesis presents two novel methodologies that enhance large-scale emission modeling by integrating deep learning into the workflow. The first methodology, SPG-M, bridges the gap between mesoscopic traffic models and microscopic emission models, a combination previously considered unfeasible due to data compatibility issues. The key innovation is a deep learning-based speed profile generator, trained on real-world driving data, which transforms mesoscopic traffic model outputs into the instantaneous speed profiles required by microscopic emission models. This integration enables highly detailed emission estimations at the local level by accounting for all speed variations within a link while providing accurate global estimates.The second methodology combines a mesoscopic traffic model with a deep learning-based mesoscopic emission model. The emission model is trained on synthetic emission data generated by a microscopic emission model. This methodology offers accuracy comparable to SPG-M while significantly improving computational efficiency.Both methodologies were successfully applied in the Île-de-France region, home to over 12 million inhabitants, to predict CO2 and NOx emissions, demonstrating their scalability and capacity to maintain high granularity in large urban areas. The validation process involved evaluating each component of the proposed methodologies separately. Finally, emissions were compared to well-established macroscopic emission models, namely HBEFA and COPERT, as well as to data from emission measurement campaigns. Results indicate that while global CO2 emissions are at comparable levels across all models, macroscopic models underestimate NOx emissions. Discrepancies are also observed at local levels, as macroscopic models fail to capture high- and low-acceleration zones, which are responsible for high- and low-emission zones, respectively

L'usage excessif des routes peut entraîner de nombreux inconvénients dont la pollution, les accidents et la congestion. Une solution accessible à court terme consiste à mettre en œuvre des systèmes de gestion de trafic. Dans ce cadre, nous proposons un formalisme, appelé Réseaux de Petri Lots Triangulaire, qui permet la modélisation et la simulation du trafic routier au niveau mésoscopique comme un système à événements discrets. Le RdPLots Triangulaire permet ainsi de décrire les caractéristiques globales du trafic routier: flux, densité et vitesse à travers la proposition d'une relation flux-densité triangulaire. Cette relation implique une modification de la dynamique des lots. Cette dynamique permet maintenant de représenter les deux états du trafic routier à savoir fluide et congestionné ainsi que les trois régimes dédiés au comportement libre, congestion et décongestion. Le calcul des flux instantanés des transitions est à présent réalisé par une méthode basée sur la technique de programmation linéaire en ajoutant une contrainte qui prend en compte l'état et le régime des lots. Pour modéliser des stratégies de contrôle telles que la variation de la vitesse limite (VSL), nous avons intégré au RdPLots Triangulaire des événements contrôlés qui permettent le changement de la vitesse maximale d'une place lot et le flux maximal d'une transition continue ou lot. Tous ces apports théoriques sont implémentés dans un logiciel que nous avons appelé SimuleauTri, sous lequel nous avons étudié des portions d'autoroute à partir des données réelles. Les résultats de simulation sont proches des mesures effectuées sur le terrain, et montrent la pertinence de RdPLots Triangulaire<br>The excessive use of roads can cause many adverse effects including pollution, insecurity and congestion. The available short-term solution is the implementation of traffic management systems which optimize the flow and reduce congestion without needing additional infrastructures. In this context, we proposed a new formalism, called Triangular Batches Petri Nets (Triangular BPN), which combines modeling and simulation of traffic in mesoscopic level as a discrete event system. The Triangular BPN describing the overall characteristics of the road traffic such as flow, density, speed by representing a new triangular relation flow-density. This relation implies the modification of batches dynamic, which is now used to represent the two road traffic states : fluid and congested, as well as the three behaviors :free, congestion and decongestion. The calculation of the instantaneous firing flows is achieved by adding a constraint that takes into account the state and behavior of batches. A set of controlled events integrated to the Triangular BPN, that allow the variation of the maximum speed of batch place and the maximum flow of batch and continuous transition. These controlled events used to model the control strategies, such as variable speed limit (VSL). All these theoretical contributions implemented in a software that is called SimuleauTri and used to study a motorway portions from real data. The simulation results are close to the measurements on the ground and show the pertinence of Triangular BPN

Modeling of heterogeneous driver behaviour is vital to understanding of dynamic traffic phenomenon taking place on motorway networks. In this research, we present a mesoscopic whole link continuous vehicle bunch model for multiclass traffic flow simulation on motorway networks. Two main attributes of traffic flow classification have been used are: (i) vehicle type, specifying in turn a vehicle length and, together with type of a preceding vehicle, time headway; and, (ii) desired speed, defining together with the speeds of the neighbouring vehicles, the vehicle acceleration/deceleration mode. It is assumed that vehicles in uncongested to moderate congested flow move in bunches dividing the drivers into the two main groups: (i) independent “free” drivers which usually manifest themselves as leaders of bunches; and, (ii) followers, or drivers which adapt their speed to the leader’s speed and follow each other at constrained headways specified by predecessor/successor pairs. The model proposes a solution to arbitrary traffic queries involving a motion in bunches having various speed and size by assuming the rate of driver arrivals follows semi-Poisson distribution and proportion of free drivers is predefined. The solution, assuming limited overtaking possibilities for all drivers, involves formation of longer queue behind bunches moving with slower speed and transformation of some of the “leaders” into “followers” because of adjustment their speed to the speed of the preceding slow-moving bunches. The present solution considers both stochastic and deterministic features of traffic flow and, therefore, may be easily extended to a specific uncertainty level.