Public transportation stands a very important role in the modern society. It solves many transportation problems, reduces the negative impacts of motor vehicles to the environment, and brings benefit and mobility to everyone. Public transportation models are used in urban area transit networks in order to predict the future impacts of the traffic policy and changes. Models in public transportation simulation are extensively studied in the literature, but very few have compared all the available models to a traffic data to find which model is the most accurate one in simulating a transit network. This study provides an overview of some of the available approaches in public transportation modeling, describes how they work by both theoretical review and examples. On the analysis, the modeling results from each model are compared with the Stockholm traffic data. The Stockholm traffic data of average weighted travel time, travel distance, in-vehicle travel distance and number of transfers are extracted from the RES05/06 survey data. The model which provides modeling outputs with least root mean square error compared to the survey data is found. This study could give an initial suggestion for the same type of transit modeling in choosing the appropriate model and finding the direction for calibrating the parameter settings through our proposed comparison measures.

In this study, a comprehensive set of transit, intermodal and multimodal assignment models (FAST-TrIPs) is developed for transportation planning and operations purposes. The core part of the models is a schedule-based transit assignment with capacity constraint and boarding priority. The problem is defined to model the system performance dynamically by taking into account the scheduled transit service and to model user behavior more realistically by taking into account capacity of transit vehicles and boarding priority for passengers arriving early to a stop or a transfer point. An optimization model is proposed for both deterministic and stochastic models, which includes a penalty term in the objective function to model the boarding priority constraint. The stochastic model is proposed based on logit route choice with flexibility on the level of stochasticity in route choice. Optimality conditions show that the models are consistent with network equilibrium and user behavior. An extension of the optimization models is proposed for multimodal assignment problem, in which the transit and auto networks interact dynamically. To solve the proposed models, since the penalty term is non-linear and makes the model an asymmetric nonlinear optimization model with side constraints, a simulation-based approach is developed. The solution method incorporates the path assignment models and a mesoscopic transit passenger simulation in conjunction with Dynamic Traffic Assignment (DTA) models. The simulation model can capture detailed activities of transit passengers and determines the nonlinear penalty explicitly by reporting passengers' failure to board experience. Therefore, the main problem can be solved iteratively, by solving a relaxed problem and simulating the transit network in each iteration, until the convergence criterion is met. The relaxed problem is a path generation model and can be either a shortest/least-cost path or a logit-based hyperpath in the schedule-based transit network. An efficient set of path models are developed using Google's General Transit Feed Specification (GTFS) files, taking into account the transit network hierarchy for computational efficiency of the model. A multimodal assignment model is also developed by integration of the proposed transit assignment model with DTA models. The model is based on simulation and is able to capture the effect of transit and auto mode on each other through an iterative solution method and feedback loop from the transit assignment model to the DTA models. In the multimodal assignment model, more realistic transit vehicle trajectories are generated in the DTA models and are used for assigning transit passengers to transit vehicles. In an application of the multimodal assignment, intermodal tours are modeled considering the timing of auto trips and transit connections, the schedule-based transit network, and the constraint on park-n-ride choice in a tour. The model can simulate the transit, auto, and intermodal tours together with high resolution and realistic user behavior.

Despite the continued interest in transportation accessibility, it is still unclear how different types of accessibility measures relate to one another and which situations are best for each. The current study undertakes a statistical comparison among six transit accessibility measures (representing three main categories of accessibility models) to determine whether they are comparable and/or interchangeable. Specifically, this analysis considers a case study to measure individuals’ access to healthcare via paratransit. Results indicate that the three categories of accessibility measures provide drastically different interpretations of accessibility that cannot be duplicated by each other. Furthermore, the more closely accessibility models capture individuals’ perceptions and true access to activity opportunities, the more consistent and evenly distributed the results. / text

Les villes sont des systèmes complexes que les modèles urbains peuvent aider à comprendre. Des modèles les plus simplistes aux modèles les plus sophistiqués, la modélisation urbaine a permis de mieux comprendre la question urbaine et ses implications sociétales. Dans ce contexte, les modèles peuvent avoir une valeur-ajoutée appréciable dans le processus de décision publique. Encore faut-il que ces modèles deviennent pratiques et répondent aux contraintes opérationnelles de la chaîne de décision. Dans ce sens, peu de recherches s’est intéressée à la question de praticité des modèles urbains et leur utilisation en situation opérationnelle. À ce jour, les modèles urbains standard qui reposent sur une description agrégée de l’espace sont parmi les approches de modélisation les plus opérationnelles et aussi les plus répandues. De par sa relative praticité, cette approche standard est attractive et simple à mettre en oeuvre. Toutefois, l’agrégation spatiale peut aussi être une source de biais statistiques préjudiciables à la qualité de la modélisation. C’est en particulier, le cas des modèles intégrés Transport-Urbanisme ou des modèles de transport à quatre étapes.La présente thèse a pour objectif d’étudier la question de l’agrégation spatiale dans les modèles transport et plus particulièrement dans les modèles d’affectation des déplacements. Les modèles d’affectation servent à calculer les temps de parcours et les conditions de déplacement sous congestion, présents et futurs, des personnes et des marchandises. Ils servent aussi à calculer les accessibilités nécessaires aux modèles d’usage des sols dont les modèles de choix de localisation des ménages et des entreprises. Toute erreur ou biais dans l’affectation des déplacements peut compromettre la validité et la qualité globales de la modélisation. Dans ce cadre, une attention particulière doit être allouée au problème d’agrégation spatiale dans les modèles d’affectation. Dans ces modèles, l’agrégation spatiale consiste à regrouper les observations individuelles enutilisant une description agrégée de l’espace, i.e. des zones. Par nature, l’utilisation d’une description agrégée à la place d’une représentation continue engendre une omission de l’information et de sa variabilité et donc un biais statistique dans la modélisation. C’est le cas par exemple avec l’utilisation des connecteurs de zones ou avec l’omission des trafics intrazones dans les modèles d’affectation.En reposant sur les zones comme unité spatiale de base, les modèles de transport recourent à l’utilisation des connecteurs de zones pour relier les centroïdes de zones au réseau de transport. Les connecteurs sont des liens fictifs qui modélisent les conditions moyennes d’entrée et de sortie du réseau de transport. Pour ce faire, la majorité des modèles de transport reposent sur une méthode simpliste sujette au problème d’agrégation spatiale. La présente thèse examine en détail l’impact de cette description simpliste sur les résultats et la qualité d’un modèle d’affectation des déplacements en transports en commun. Cette thèse propose aussi une nouvelle méthode de modélisation des connecteurs de zones afin de s’affranchir partiellement du biaisd’agrégation spatiale dans la modélisation des conditions d’accès au réseau des transports en commun.L’utilisation des zones comme unité spatiale de base a aussi pour conséquence l’omission des trafics intrazones de l’affectation des déplacements. Les trafics intrazones ont pour origine et pour destination la même zone et de ce fait ne sont pas pris en compte par les modèles standard d’affectation. Cette omission a souvent été ignorée et son impact sur la qualité de la modélisation demeure non évalué. Cette thèse développe une méthode stochastique pour l’évaluation de cet impact... / Cities are complex systems that urban models can help to comprehend. From simplistic models to more sophisticated ones, urban models have pushed forward our understanding the urban phenomenon and its intricacies. In this context, models can be of great value to policy makers providing that these tools become practical. In this regard, research has put little emphasis on the practicality of urban models and their use under operational conditions.To date, urban models which rely on spatial aggregation are the closest possibility to come to practical models. For this reason, the spatially aggregated modeling framework is widely used. This framework is relatively practical when compared to other modeling frameworks like microsimulation. Nevertheless, spatial aggregation is a serious source of bias in these models. This is especially the case of Land-Use and Transport Interaction (LUTI) models and more particularly of Four Step Models.The current PhD is committed to the study of spatial aggregation issues in traffic assignment models. Traffic assignment is responsable for the computation of travel times and travel conditions of present and future travel demand. Accessibility measurement, which is at the core of LUTI models, is tightly dependent on traffic assignment modeling and outcomes. Any bias in traffic assignment is likely to corrupt the overall modeling framework. In this context, a special attention is to be paid to spatial aggregation in traffic assignment models.In traffic assignment, spatial aggregation consists in grouping observations using zones or traffic analysis zones instead of using a continuous representation of space. By design, aggregation bears an implicit omission in data variability and thus a potential bias if this omission is not random. This is the case with the definition of centroid connectors and the omission of intrazonal demand in traffic assignment. With the use of zones as the basic spatial units, transport models require the use of centroid connectors to attach zones to the transportation network. Centroid connectors are introduced to model average access and egress conditions to and from the network. Nevertheless, average accessibility conditions are found to be too crude to render accurately accessibility conditions as encountered by trip makers. The current PhD explores the extent of the impact of this spatial aggregation bias in the case of transit models and suggests a new modeling strategy to overcome such modeling errors.The use of zones as spatial units induces a loss of intrazonal data. The omission of intrazonal trips in traffic assignment models is an example of such omission. This research introduces an uncertainty framework to study the statistical impact of ignoring intrazonal trips in traffic assignment models. Findings from this research are used to design new assignment strategies that are more robust towards the omission bias and more generally towards the spatial aggregation bias.

Connected and automated vehicles (CVs and AVs, respectively) are rapidly emerging paradigms aiming to deploy and develop transportation systems that enable automated driving and data exchange among vehicles, infrastructure, and mobile devices to improve mobility, enhance safety, and reduce the adverse environmental impacts of transportation systems. Based on these premises, the focus of this research is to quantify the potential benefits of CVs and AVs to provide insight into how these technologies will impact road users and network performance. To assess the traffic operational performance of CVs, a connectivity-based modeling framework was developed based on traffic microsimulation for a real network in the city of Toronto. Then the effects of real-time routing guidance and advisory warning messages were studied for CVs. In addition, the impact of rerouting of non-connected vehicles (non-CVs) in response to various sources of information, such as mobile apps, GPS or VMS, was considered and evaluated. The results demonstrate the potential of such systems to improve mobility, enhance safety, and reduce greenhouse gas emissions (GHGs) at the network-wide level presented for different CVs market penetration. Additionally, the practical application of CVs in travel time estimation and its relationship with the number and location of roadside equipment (RSE) along freeways was investigated. A methodology was developed for determining the optimal number and location of roadside equipment (RSE) for reducing travel time estimation error in a connected vehicle environment. A simulation testbed that includes CVs was developed and implemented in the microsimulation model for Toronto 400-series highway network. The results reveal that the suggested methodology is capable of optimizing the number and location of RSEs in a connected vehicle environment. The optimization results indicate that the accuracy of travel time estimates is primarily dependent on the location of RSEs and less dependent on the total density of RSEs. In addition to CVs, the potential capacity increase of highways as a function of AVs market penetration was also studied and estimated. AVs are classified into Cooperative and Autonomous AVs. While Autonomous AVs rely only to their detection technology to sense their surroundings, Cooperative AVs, can also benefit from direct communication between vehicles and infrastructure. Cooperative car-following and lane-changing models were developed in a microsimulation model to enable AVs to maintain safe following and merging gaps. This study shows that cooperative AVs can adopt shorter gap than autonomous AVs and consequently, can significantly improve the lane capacity of highways. The achievable capacity increase for autonomous AVs appears highly insensitive to the market penetration, namely, the capacity remains within a narrow range of 2,046 to 2,238 vph irrespective of market penetration. The results of this research provide practitioners and decision-makers with knowledge regarding the potential capacity benefits of AVs with respect to market penetration and fleet conversion. / Thesis / Doctor of Philosophy (PhD)