A identificação dos segmentos críticos da rede viária é um conhecimento básico que todo planejador de transportes deve ter sobre a rede viária. É inevitável a ocorrência de incidentes e eventos de redução da capacidade nos elementos da rede. O efeito de acidentes e obstruções em segmentos críticos da rede ocasionam impactos que prejudicam o desempenho da rede. Este estudo foi desenvolvido com o apoio do modelo de alocação de tráfego do software VISUM (versão 2015) e propõe um método para determinar a importância de cada segmento da rede viária, a identificação de segmentos críticos da rede e a avaliação de sua obstrução de forma estática e dinâmica. Para isso, são utilizados diversos indicadores de desempenho da rede viária. O método foi aplicado na região do Litoral Norte do Rio Grande do Sul, utilizando os dados de tráfego fornecidos pela CONCEPA TRIUNFO, DAER e DNIT. A determinação da importância de cada segmento decorreu da avaliação do impacto na rede causado pela obstrução do próprio segmento. Através do método proposto neste estudo foi possível identificar o segmento crítico da rede viária estudada e, de forma qualitativa, verificar a extensão da obstrução desse segmento nas análises estática e dinâmica Verificou-se que o indicador diferença do total de tempo despendido na rede é o indicador que mais apresenta crescimento com o incremento da demanda, não apresenta alterações de priorização dos segmentos frente a variações de intensidade e sentido da demanda. Os resultados deste estudo mostraram que o segmento crítico da rede pertence a BR-101 entre os municípios de Osório e Terra de Areia. O método de hierarquização proposto independe do sentido e da intensidade da demanda, e está sujeito a mais de uma métrica para avaliar o segmento crítico. Estes resultados podem subsidiar o planejamento de transportes, identificando trechos críticos da rede viária que necessitam de mais atenção dos gestores e apontando medidas de operação no caso de eventos disruptivos nos trechos críticos. / Identifying the most important link of the network is essential knowledge that the transport planners should have over the network. Incidents and events of capacity reduction in network elements are inevitable. The effect of accidents and obstructions on critical network links causes impacts that hamper network performance. This study was developed with support of VISUM (version 2015) traffic assignment model software with proposes a method to determinate each network link importance level, to identify the critical link and to measure the critical link blockage impact on network. For this, several road network performance indicators are used. The method was applied in the North Coast region of Rio Grande do Sul, using traffic data provided by CONCEPA TRIUNFO, DAER and DNIT. The link level importance in define from the own link impact due its obstruction. The proposed method identified the most critical link of the studied network and verified the qualitative impact of its obstruction extent in the static and dynamic assignment analyses It was verified that the measure total spent time difference in the network is the most sensible measure that growth with a demand increase, this measure does not present changes the link importance rank against variations of intensity and direction of demand flow changes. The results of this study show that the critical link of the network belongs to the BR-101 highway between the municipalities of Osório and Terra de Areia. The proposed hierarchical method developed with several metrics measures fond the critical link in an independent demand direction and intensity analysis. The findings may support transport planners to identify the most critical arc of a network. To better implement resources of road management and repairs. Also identify where the operation measures may be implanted in face a disruptive event on a critical link.

Advanced modeling tools and methods are essential components for the analyses of congested conditions and advanced Intelligent Transportation Systems (ITS) strategies such as Managed Lanes (ML). A number of tools with different analysis resolution levels have been used to assess these strategies. These tools can be classified as sketch planning, macroscopic simulation, mesoscopic simulation, microscopic simulation, static traffic assignment, and dynamic traffic assignment tools. Due to the complexity of the managed lane modeling process, this dissertation investigated a Multi-Resolution Modeling (MRM) approach that combines a number of these tools for more efficient and accurate assessment of ML deployments. This study clearly demonstrated the differences in the accuracy of the results produced by the traffic flow models incorporated into different tools when compared with real-world measurements. This difference in the accuracy highlighted the importance of the selection of the appropriate analysis levels and tools that can better estimate ML and General Purpose Lanes (GPL) performance. The results also showed the importance of calibrating traffic flow model parameters, demand matrices, and assignment parameters based on real-world measurements to ensure accurate forecasts of real-world traffic conditions. In addition, the results indicated that the real-world utilization of ML by travelers can be best predicated with the use of dynamic traffic assignment modeling that incorporates travel time, toll, and travel time reliability of alternative paths in the assignment objective function. The replication of the specific dynamic pricing algorithm used in the real-world in the modeling process was also found to provide the better forecast of ML utilization. With regards to Connected Vehicle (CV) operations on ML, this study demonstrated the benefits of using results from tools with different modeling resolution to support each other’s analyses. In general, the results showed that providing toll incentives for Cooperative Adaptive Cruise Control (CACC)-equipped vehicles to use ML is not beneficial at lower market penetrations of CACC due to the small increase in capacity with these market penetrations. However, such incentives were found to be beneficial at higher market penetrations, particularly with higher demand levels.

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou’s model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou’s model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

A identificação dos segmentos críticos da rede viária é um conhecimento básico que todo planejador de transportes deve ter sobre a rede viária. É inevitável a ocorrência de incidentes e eventos de redução da capacidade nos elementos da rede. O efeito de acidentes e obstruções em segmentos críticos da rede ocasionam impactos que prejudicam o desempenho da rede. Este estudo foi desenvolvido com o apoio do modelo de alocação de tráfego do software VISUM (versão 2015) e propõe um método para determinar a importância de cada segmento da rede viária, a identificação de segmentos críticos da rede e a avaliação de sua obstrução de forma estática e dinâmica. Para isso, são utilizados diversos indicadores de desempenho da rede viária. O método foi aplicado na região do Litoral Norte do Rio Grande do Sul, utilizando os dados de tráfego fornecidos pela CONCEPA TRIUNFO, DAER e DNIT. A determinação da importância de cada segmento decorreu da avaliação do impacto na rede causado pela obstrução do próprio segmento. Através do método proposto neste estudo foi possível identificar o segmento crítico da rede viária estudada e, de forma qualitativa, verificar a extensão da obstrução desse segmento nas análises estática e dinâmica Verificou-se que o indicador diferença do total de tempo despendido na rede é o indicador que mais apresenta crescimento com o incremento da demanda, não apresenta alterações de priorização dos segmentos frente a variações de intensidade e sentido da demanda. Os resultados deste estudo mostraram que o segmento crítico da rede pertence a BR-101 entre os municípios de Osório e Terra de Areia. O método de hierarquização proposto independe do sentido e da intensidade da demanda, e está sujeito a mais de uma métrica para avaliar o segmento crítico. Estes resultados podem subsidiar o planejamento de transportes, identificando trechos críticos da rede viária que necessitam de mais atenção dos gestores e apontando medidas de operação no caso de eventos disruptivos nos trechos críticos. / Identifying the most important link of the network is essential knowledge that the transport planners should have over the network. Incidents and events of capacity reduction in network elements are inevitable. The effect of accidents and obstructions on critical network links causes impacts that hamper network performance. This study was developed with support of VISUM (version 2015) traffic assignment model software with proposes a method to determinate each network link importance level, to identify the critical link and to measure the critical link blockage impact on network. For this, several road network performance indicators are used. The method was applied in the North Coast region of Rio Grande do Sul, using traffic data provided by CONCEPA TRIUNFO, DAER and DNIT. The link level importance in define from the own link impact due its obstruction. The proposed method identified the most critical link of the studied network and verified the qualitative impact of its obstruction extent in the static and dynamic assignment analyses It was verified that the measure total spent time difference in the network is the most sensible measure that growth with a demand increase, this measure does not present changes the link importance rank against variations of intensity and direction of demand flow changes. The results of this study show that the critical link of the network belongs to the BR-101 highway between the municipalities of Osório and Terra de Areia. The proposed hierarchical method developed with several metrics measures fond the critical link in an independent demand direction and intensity analysis. The findings may support transport planners to identify the most critical arc of a network. To better implement resources of road management and repairs. Also identify where the operation measures may be implanted in face a disruptive event on a critical link.

Dynamic Traffic Assignment (DTA) is gaining wider acceptance among agencies and practitioners because it serves as a more realistic representation of real-world traffic phenomena than static traffic assignment. Many metropolitan planning organizations and transportation departments are beginning to utilize DTA to predict traffic flows within their networks when conducting traffic analysis or evaluating management measures. To analyze DTA-based optimization applications, it is critical to obtain the dual (or gradient) information as dual information can typically be employed as a search direction in algorithmic design. However, very limited number of approaches can be used to estimate network-wide dual information while maintaining the potential to scale. This dissertation investigates the theoretical/practical aspects of DTA-based dual approximation techniques and explores DTA applications in the context of various transportation models, such as transportation network design, off-line DTA capacity calibration and dynamic congestion pricing. Each of the later entities is formulated as bi-level programs. Transportation Network Design Problem (NDP) aims to determine the optimal network expansion policy under a given budget constraint. NDP is bi-level by nature and can be considered a static case of a Stackelberg game, in which transportation planners (leaders) attempt to optimize the overall transportation system while road users (followers) attempt to achieve their own maximal benefit. The first part of this dissertation attempts to study NDP by combining a decomposition-based algorithmic structure with dual variable approximation techniques derived from linear programming theory. One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. It is therefore imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies. Satisfactory calibration of the DTA model is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. In this dissertation, the off-line DTA capacity calibration problem is studied in an attempt to devise a systematic approach for effective model calibration. Congestion pricing has increasingly been seen as a powerful tool for both managing congestion and generating revenue for infrastructure maintenance and sustainable development. By carefully levying tolls on roadways, a more efficient and optimal network flow pattern can be generated. Furthermore, congestion pricing acts as an effective travel demand management strategy that reduces peak period vehicle trips by encouraging people to shift to more efficient modes such as transit. Recently, with the increase in the number of highway Build-Operate-Transfer (B-O-T) projects, tolling has been interpreted as an effective way to generate revenue to offset the construction and maintenance costs of infrastructure. To maximize the benefits of congestion pricing, a careful analysis based on dynamic traffic conditions has to be conducted before determining tolls, since sub-optimal tolls can significantly worsen the system performance. Combining a network-wide time-varying toll analysis together with an efficient solution-building approach will be one of the main contributions of this dissertation. The problems mentioned above are typically framed as bi-level programs, which pose considerable challenges in theory and as well as in application. Due to the non-convex solution space and inherent NP-complete complexity, a majority of recent research efforts have focused on tackling bi-level programs using meta-heuristics. These approaches allow for the efficient exploration of complex solution spaces and the identification of potential global optima. Accordingly, this dissertation also attempts to present and compare several meta-heuristics through extensive numerical experiments to determine the most effective and efficient meta-heuristic, as a means of better investigating realistic network scenarios. / text

Ce travail porte sur la quantification d'incertitude dans la modélisation des émissions de polluants atmosphériques dues au trafic routier d'une aire urbaine. Une chaîne de modélisations des émissions de polluants atmosphériques est construite, en couplant un modèle d’affectation dynamique du trafic (ADT) avec un modèle de facteurs d’émission. Cette chaîne est appliquée à l’agglomération de Clermont-Ferrand (France) à la résolution de la rue. Un métamodèle de l’ADT est construit pour réduire le temps d’évaluation du modèle. Une analyse de sensibilité globale est ensuite effectuée sur cette chaîne, afin d’identifier les entrées les plus influentes sur les sorties. Enfin, pour la quantification d’incertitude, deux ensembles sont construits avec l’approche de Monte Carlo, l’un pour l’ADT et l’autre pour les émissions. L’ensemble d’ADT est évalué et amélioré grâce à la comparaison avec les débits du trafic observés, afin de mieux échantillonner les incertitudes / This work focuses on the uncertainty quantification in the modeling of road traffic emissions in a metropolitan area. The first step is to estimate the time-dependent traffic flow at street-resolution for a full agglomeration area, using a dynamic traffic assignment (DTA) model. Then, a metamodel is built for the DTA model set up for the agglomeration, in order to reduce the computational cost of the DTA simulation. Then the road traffic emissions of atmospheric pollutants are estimated at street resolution, based on a modeling chain that couples the DTA metamodel with an emission factor model. This modeling chain is then used to conduct a global sensitivity analysis to identify the most influential inputs in computed traffic flows, speeds and emissions. At last, the uncertainty quantification is carried out based on ensemble simulations using Monte Carlo approach. The ensemble is evaluated with observations in order to check and optimize its reliability

La croissance démographique dans les zones urbaines représente un problème pour la planification des transports. La surcharge des systèmes de transport urbains entraîne des coûts monétaires importants et des problèmes environnementaux. Des mesures politiques sont alors nécessaires pour réduire le niveau de congestion et accroître l'efficacité des systèmes de transport. À court terme, les simulateurs de trafic pourraient constituer un outil puissant pour la conception de solutions innovantes. Mais les simulateurs de trafic classiques sont exigeants sur le plan informatique pour les applications à grande échelle. De plus, la mise en place du scénario de simulation est complexe. Une modélisation de trafic agrégée pourrait être une bonne solution (Daganzo-2007, Geroliminis-2008). Le réseau routier des villes est divisé en régions, où un diagramme fondamental macroscopique bien défini (MFD) régule les conditions de circulation à l'intérieur de chacune. Le MFD concerne le débit et la densité de trafic moyens dans une région. Malgré que l’idée d’agréger le réseau de la ville soit simple, il soulève plusieurs défis qui n’ont pas encore été abordés. Jusqu'à aujourd'hui, seule (Yildirimoglu-2014) propose un cadre d'affectation dynamique du trafic pour les réseaux régionaux et les modèles MFD. Ce cadre est basé sur le modèle Logit multinomial et ne traite pas explicitement des distributions de longueurs de parcours. De plus, leur structure ne considère pas que les utilisateurs sont différents les uns des autres et ont des objectifs et des préférences différents pour leurs voyages. L'objectif de cette thèse est double. Tout d'abord, l'influence du comportement des utilisateurs sur la performance globale du réseau routier d’une ville est étudiée. Cette analyse se concentre sur la vitesse moyenne du réseau et ses capacités internes et de sortie, en comparant différents modèles tenant compte des différents types de comportement des utilisateurs par rapport à l'équilibre utilisateur déterministe et stochastique. En second lieu, un cadre innovant et complet d’affectation dynamique du trafic pour les modèles multirégionaux basés sur le MFD est proposé. Ce cadre est divisé en plusieurs étapes et repose sur les connexions entre la ville et les réseaux régionaux. Dans un premier temps, des méthodes systématiques de mise à l’échelle sont proposées pour rassembler les voies régionales. Dans un deuxième temps, quatre méthodes sont discutées pour calculer les distributions de longueurs de parcours pour caractériser ces chemins régionaux. Dans la troisième étape, un modèle de chargement de réseau qui considère les distributions de longueurs de parcours explicitement calculées et l’évolution des vitesses moyennes régionales est proposé. Enfin, ce cadre d'affectation dynamique du trafic est étendu pour prendre en compte les usager qui ont une aversion au regret ou une rationalité imparfaite. Cette thèse s'inscrit dans le cadre d'un projet européen ERC intitulé MAGnUM: approche de modélisation du trafic multi-échelle et multimodal pour la gestion durable de la mobilité urbaine. / The population growth in urban areas represents an issue for transportation planning. This overload of urban transportation systems, leading to significant monetary costs and environmental issues. Policy measures are then needed to decrease the level of congestion and increase the efficiency of transportation systems. In a short term, traffic simulators might be a powerful tool that helps to design innovative solution. But, the classical traffic simulators are computationally demanding for large scale applications. Moreover, the set up of the simulation scenario is complex. An aggregated traffic modeling might be a good solution (Daganzo, 2007; Geroliminis and Daganzo, 2008). The city network is divided into regions where a well-defined Macroscopic Fundamental Diagram (MFD) regulates the traffic conditions inside each one. The MFD relates the average traffic flow and density inside a region. Despite the idea of aggregating the city network is simple, it brings several challenges that have not yet been addressed. Up to today, only Yildirimoglu and Geroliminis (2014) proposed a dynamic traffic assignment framework for regional networks and MFD models. This framework is based on the simple Multinomial Logit model and does not explicitly deal with trip length distributions. Moreover, their framework does not consider that users are different from each other and have different purposes and preferences for their travels. The goal of this PhD dissertation is to twofold. First, the influence of the users behavior on the global network performance is investigated. This analysis focus on the network mean speed and its internal and outflow capacities, comparing different models that account for different kinds of users behavior against the Deterministic and Stochastic User Equilibrium. Second, an innovative and complete dynamic traffic assignment framework for multi-regional MFD-based models is proposed. This framework is divided into several milestones and is based on the connections between the city and regional networks. In a first step, systematic scaling-up methods are proposed to gather the regional paths. In a second step, four methods are discussed to calculate the distributions of trip lengths that characterize these regional paths. In the third step, a network loading model that considers distributions of trip lengths that are explicitly calculated and the evolution of the regional mean speeds is proposed. Finally, this dynamic traffic assignment framework is extended to account for bounded rational and regret-averse users. This PhD is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility.

This research contributes to the state of the art and state of the practice in solving a very important and computationally challenging problem in the areas of urban transportation systems, operations research, disaster management, and public policy. Being a very active topic of research during the past few decades, the problem of developing an efficient and practical strategy for evacuation of real-sized urban traffic networks in case of disasters from different causes, quickly enough to be employed in immediate disaster management scenarios, has been identified as one of the most challenging and yet vital problems by many researchers. More specifically, this research develops fast methods to find the optimal integrated strategy for traffic routing and traffic signal control to evacuate real-sized urban networks in the most efficient manner. In this research a solution framework is proposed, developed and tested which is capable of solving these problems in very short computational time. An efficient relaxation-based decomposition method is proposed, implemented for two evacuation integrated routing and signal control model formulations, proven to be optimal for both formulations, and verified to reduce the computational complexity of the optimal integrated routing and signal control problem. The efficiency of the proposed decomposition method is gained by reducing the integrated optimal routing and signal control problem into a relaxed optimal routing problem. This has been achieved through an insight into intersection flows in the optimal routing solution: in at least one of the optimal solutions of the routing problem, each street during each time interval only carries vehicles in at most one direction. This property, being essential to the proposed decomposition method, is called "unidirectionality" in this dissertation. The conditions under which this property exists in the optimal evacuation routing solution are identified, and the existence of unidirectionality is proven for: (1) the common Single-Destination System-Optimal Dynamic Traffic Assignment (SD-SODTA) problem, with the objective to minimize the total time spent in the threat area; and, (2) for the single-destination evacuation problem with varying threat levels, with traffic models that have no spatial queue propagation. The proposed decomposition method has been implemented in compliance with two widely-accepted traffic flow models, the Cell Transmission Model (CTM) and the Point Queue (PQ) model. In each case, the decomposition method finds the optimal solution for the integrated routing and signal control problem. Both traffic models have been coded and applied to a realistic real-size evacuation scenario with promising results. One important feature that is explored is the incorporation of evacuation safety aspects in the optimization model. An index of the threat level is associated with each link that reflects the adverse effects of traveling in a given threat zone on the safety and health of evacuees during the process of evacuation. The optimization problem is then formulated to minimize the total exposure of evacuees to the threat. A hypothetical large-scale chlorine gas spill in a high populated urban area (downtown Tucson, Arizona) has been modeled for testing the evacuation models where the network has varying threat levels. In addition to the proposed decomposition method, an efficient network-flow solution algorithm is also proposed to find the optimal routing of traffic in networks with several threat zones, where the threat levels may be non-uniform across different zones. The proposed method can be categorized in the class of "negative cycle canceling" algorithms for solving minimum cost flow problems. The unique feature in the proposed algorithm is introducing a multi-source shortest path calculation which enables the efficient detection and cancellation of negative cycles. The proposed method is proven to find the optimal solution, and it is also applied to and verified for a mid-size test network scenario.

Transport demand planning in South Africa is a neglected field of study, using obsolete methods to model an extremely complex, dynamic system composed of an eclectic mix of First and Third World transport technologies, infrastructure and economic participants. We identify agent-based simulation as a viable modelling paradigm capable of capturing the effects emerging from the complex interactions within the South African transport system, and proceed to implement the Multi-Agent Transport Simulation Toolkit (MATSim) for South Africa's economically important Gauteng province. This report describes the procedure followed to transform household travel survey, census and Geographic Information System (GIS) data into an activity-based transport demand description, executed on network graphs derived from GIS shape files. We investigate the influence of network resolution on solution quality and simulation time, by preparing a full network representation and a small version, containing no street-level links. Then we compare the accuracy of our data-derived transport demand with a lower bound solution. Finally the simulation is tested for repeatability and convergence. Comparisons of simulated versus actual traffic counts on important road network links during the morning and afternoon rush hour peaks show a minimum mean relative error of less than 40%. Using the same metric, the small network differs from the full representation by a maximum of 2% during the morning peak hour, but the full network requires three times as much memory to execute, and takes 5.2 times longer to perform a single iteration. Our census- and travel survey-derived demand performs significantly better than uniformly distributed random pairings of home- and work locations, which we took to be analogous to a lower bound solution. The smallest difference in corresponding mean relative error between the two cases comes to more than 50%. We introduce a new counts ratio error metric that removes the bias present in traditional counts comparison error metrics. The new metric shows that the spread (standard deviation) of counts comparison values for the random demand is twice to three times as large as that of our reference case. The simulation proves highly repeatable for different seed values of the pseudo-random number generator. An extended simulation run reveals that full systematic relaxation requires 400 iterations. Departure time histograms show how agents 'learn' to gradually load the network while still complying with activity constraints. The initial implementation has already sparked further research. Current priorities are improving activity assignment, incorporating commercial traffic and public transport, and the development and implementation of the minibus taxi para-transit mode. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted

L’objectif de ce travail consiste en la modélisation des flux de véhicules d’un grand et dense réseau de transport multimodal. Le travail s’organise en deux parties: un aspect théorique et un aspect développement. L’étude théorique met l’accent sur la façon dont un réseau multimodal peut être modélisé et comment sa performance en termes d’offre peut être optimisée. Pour ce faire, trois études principales sont réalisées: la prévision et la régulation des flux de trafic sur les grands réseaux de surface, la multimodalité véhiculaire dans les grands réseaux de surface prenant en compte les nouvelles formes de mobilité, et enfin l’impact de l’information sur le coût des itinéraires. La partie développement consiste en la conception d’un simulateur de flux de trafic pour réguler le trafic multimodal véhiculaire. Le simulateur développé devrait aider les opérateurs de transport et les collectivités territoriales dans leurs stratégies de gestion des flux de trafic / The objective of this work consists on the modeling of traffic flow of a large multimodaltransportation network. The work is organized in two parts: a theoretical study part anda development part. The theoretical study emphasizes on how a multimodal network canbe model and how its performance in terms of supply can be optimized. To do so, threemain studies are discussed: the traffic flow prediction and regulation on large surface net-works, the vehicular multimodality in big surface networks taking into account new forms ofmobility, and finally the impact of the information on the cost of the itineraries. The devel-opment part consists on the conception of a traffic flow simulator to regulate the vehicularmultimodal traffic. The developed simulator should assist transport operators and territorialcommunities in their traffic flow management strategies