This dissertation focuses on modeling parking search behavior in traffic assignment models. Parking contributes greatly to urban traffic congestion. When the parking supply is scarce, it is very common for a vehicle to circle around for a considerable period just for an open parking spot. This circling or "cruising" add additional traffic flow onto the network. However, traditional traffic assignment models either ignore parking completely or simply treat it in limited ways. Most traffic assignment models simply assume travelers just directly drive from their origin to their destination without considering the parking search behavior. This would result in a systematic underestimation of road traffic flows and congestion which may mislead traffic managers to give inappropriate planning or control strategies. Models which do incorporate parking effects either constrain their implementation in limited small networks or ignore the stochasticity of parking choice by drivers. This dissertation improves upon previous research into network parking modeling, explicitly capturing drivers' behavior and stochasticity in the parking search process, and is applicable to general networks. This dissertation constructs three types of parking search models. The first one is to model a single driver's parking search process, taking into account the likelihood of finding parking in different locations from past experience as well as observations gained during the search itself. This model uses the a priori probability of finding parking on a link, which reflects the average possibility of finding a parking space based on past experience. This probability is then adjusted based on observations during the current search. With these concepts, the parking search behavior is modeled as a Markov decision process (MDP). The primary contribution of the proposed model is its ability to reflect history dependence which combines the advantages of assuming "full reset" and "no reset" . "Full reset" assumes the probability of finding a parking space on a link is independent of any observations in the current search, while "no reset" assumes the state of parking availability is completely determined by past observations, never changing once observed. For instance, assume that the a priori probability of finding parking on a link is 30%. "Full reset" implies that if a driver drives on this link and sees no parking available, if he or she immediately turns around and drives on the link again, the probability of finding parking is again 30% independent of the past observation. By contrast, "no reset" implies that if a parking space is available on a link, it will always be available to return to in the future at any point. This dissertation develops an "asymptotic reset" principle which generalizes these principles and allows past observations to affect the probability of finding parking on a link and this impact weakens as time goes by. Both full reset and no reset are shown to be special cases of asymptotic reset. The second problem is modeling multiple drivers through a parking search equilibrium on a static network. Similar to the first type of problem, drivers aim to minimize their total travel costs. Their driving and parking search behaviors depend on the probabilities of finding parkings at particular locations in the network. On the other side, these probabilities depend on drivers' route and parking choices. This mutual dependency can be modeled as an equilibrium problem. At the equilibrium condition no driver can improve his or her expected travel cost by unilaterally changing his or her routing and parking search strategy. To accomplish this, a network transformation is introduced to distinguish between drivers searching for parking on a link and drivers merely passing through. The dependence of parking probability on flow rates results in a set of nonlinear flow conservation equations. Nevertheless, under relatively weak assumptions the existence and uniqueness of the network loading can be shown, and an intuitive 'flow-pushing" algorithm can be used to solve for the solution of this nonlinear system. Built on this network loading algorithm, travel times can be computed. The equilibrium is formulated as a variational inequality, and a heuristic algorithm is presented to solve it. An extensive set of numerical tests shows how parking availability and traffic congestion (flows and delays) vary with the input data. The third problem aims at developing a dynamic equivalent for the network parking search equilibrium problem. This problem attempts to model a similar set of features as the static model, but aims to reflect changes in input demand, congestion, and parking space availability over time. The approach described in the dissertation is complementary to the static approach, taking on the flavor of simulation more than mathematical formulation. The dynamic model augments the cell transmission model with additional state variables to reflect parking availability, and integrates this network loading with an MDP-based parking search behavior model. Finally, case studies and sensitivity analysis are taken for each of the three models. These analyses demonstrate the models' validity and feasibility for practice use. Specifically, all the models show excess travel time and flow on the transportation networks because of taking into account the "parking search cruising" and can show the individual links so affected. They all reflect the scattered parking distribution on links while traditional traffic assignment models only assign vehicles onto specified destination nodes. / text

Vehicles searching for on-street parking create environmental and economic externalities through increasing network traffic flow and congestion, heightening pollutant emission levels, creating additional noise, giving rise to time delays for through vehicles, and leading to potential safety hazards caused by vehicles manoeuvring into or out of on-street spaces. Despite extensive negative impacts on individual drivers and on society, parking search is an under-researched area, particularly in more recent years and within the UK. Furthermore, current statistical modelling techniques applied to parking search time have not utilised a more comprehensive analysis in which hierarchically structured data on multiple levels could be addressed. The aim of this thesis, therefore, is to investigate and compare the factors that influence drivers urban on-street parking search time and its policy implications. A mixed methods approach was applied that comprised qualitative interviews conducted with local government authority Council Officers and a quantitative revealed preference on-street parking survey (sample size, 1,002 observations) undertaken in four cities in the East Midlands region of the UK in order to obtain individual driver-level socio-economic and other parking related factors that may influence parking search time. Statistically significant variables for each of the cities were identified by employing separate linear regression models. A multilevel mixed-effects model in which drivers (Level 1) are nested within streets (Level 2) was then applied to the pooled dataset. Significant factors in the multilevel (street level) model were identified as: time of arrival at a parking place (for which every time period after the 07:00-07:59 reference case indicated increased search time); parking habit; parking tariff; the number of parking places previously visited (on the same trip); trip time from origin to parking place; area type; trip purpose; weather; vehicle type; and walking time from a parking place to a destination. Comparison of the factors that influence parking search time revealed important differences in statistically significant variables and coefficient values between the single-level and multilevel regression modelling approaches. Policy recommendations based upon the findings of the parking survey, modelling analysis, and further interviews conducted with local authority Council Officers, focus around time of arrival at a parking place, area type, parking charges and the potential technological advances that, if implemented, could have a considerable effect on parking search times within urban areas. Robust data collection and subsequent monitoring of parking search activity within each city should be undertaken in order to provide an evidence base which would support the introduction of future policy measures to reduce parking search activity.

abstract: This dissertation investigates congestion mitigation during the ingress of a planned special event (PSE). PSEs would impact the regular operation of the transportation system within certain time periods due to increased travel demand or reduced capacities on certain road segments. For individual attendees, cruising for parking during a PSE could be a struggle given the severe congestion and scarcity of parking spaces in the network. With the development of smartphones-based ridesharing services such as Uber/Lyft, more and more attendees are turning to ridesharing rather than driving by themselves. This study explores congestion mitigation during a planned special event considering parking, ridesharing and network configuration from both attendees and planner’s perspectives. Parking availability (occupancy of parking facility) information is the fundamental building block for both travelers and planners to make parking-related decisions. It is highly valued by travelers and is one of the most important inputs to many parking models. This dissertation proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility. From an attendee’s perspective, the probability of finding parking at a particular parking facility is more treasured than occupancy information for parking search. However, it is hard to estimate parking probabilities even with accurate occupancy data in a dynamic environment. In the second part of this dissertation, taking one step further, the idea of introducing learning algorithms into parking guidance and information systems that employ a central server is investigated, in order to provide estimated optimal parking searching strategies to travelers. With the help of the Markov Decision Process (MDP), the parking searching process on a network with uncertain parking availabilities can be modeled and analyzed. Finally, from a planner’s perspective, a bi-level model is proposed to generate a comprehensive PSE traffic management plan considering parking, ridesharing and route recommendations at the same time. The upper level is an optimization model aiming to minimize total travel time experienced by travelers. In the lower level, a link transmission model incorporating parking and ridesharing is used to evaluate decisions from and provide feedback to the upper level. A congestion relief algorithm is proposed and tested on a real-world network. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

La gestion du stationnement se présente comme un levier phare pour l’orientation modale des déplacements urbains : en effet les restrictions par la rareté ou le prix pénalisent le mode automobile et renforcent l’attrait des autres modes de déplacement, plus économes en impacts sur l’environnement. Cependant une gestion restrictive augmente la difficulté de trouver une place, les parcours de recherche et les gênes à la circulation, ce qui accroît l’impact environnemental de chacun des déplacements automobiles subsidiaires. Cette thèse traite la problématique quantitative de l’offre et de la demande de stationnement automobile en milieu urbain, au prisme de l’éco-conception pour rendre la planification plus soutenable. Elle propose un modèle de simulation qui permet d’étudier des plans de gestion de stationnement, en privilégiant le fonctionnement technique du système et son interaction avec la circulation. La première partie propose une analyse de l’évolution du stationnement urbain dans les politiques publiques en France, suivie d’une analyse théorique mettant en avant les enjeux et des défis que soulève une démarche d’éco-conception. Une exploration systémique du stationnement, centrée sur ses dimensions physiques, fonctionnelles et spatiales, est ensuite présentée. De plus, une méthodologie pour diagnostiquer l’état du stationnement à l’échelle d’une agglomération, sur la base d’une Enquête Ménages Déplacements, est développée Elle est illustrée par une application à l’Île-de-France en 2010.La deuxième partie est consacrée à la modélisation spatialisée du système de stationnement. Elle commence par une revue de littérature spécifique. Puis nous proposons un traitement statique des interactions entre stationnement et circulation routière, à l’échelle locale. Un modèle spatialisé d’affection de trafic à l’équilibre (modèle ParkCap) est développé. Il permet de modéliser les choix conjoints d’itinéraire et de lot de stationnement, en considérant explicitement les contraintes de capacités de l’offre et le phénomène de recherche de places sur le réseau routier. Pour démontrer le fonctionnement du modèle deux exemples numériques sont traités, avant de décrire brièvement la structure du simulateur informatique. La troisième et dernière partie fournit une application du modèle à la planification stratégique du stationnement dans le quartier de la cité Descartes. Après un diagnostic territorial du site et de son système de stationnement, une simulation de l’état de référence de 2010 est mise en œuvre. L’application permet d’illustrer la méthode opératoire et de démontrer les capacités de l’outil ParkCap à reproduire une situation réelle et d’évaluer plusieurs variantes alternatives de gestion locale de stationnement. Nous l’étendons à une étude prospective de l’évolution du système de stationnement à l’horizon de 2030. En anticipant les transformations urbaines associées au projet urbain du Grand Paris Express, trois scénarios contrastés d’offre de stationnement sont comparés et évalués / Parking management is a key lever for the modal orientation of urban travel. Indeed, restrictions by scarcity or price penalize the automobile mode and increase the attractiveness of alternative travel modes, which have less impact on the environment. However, restrictive management increases the difficulty of finding an available spot, cruising for parking and traffic congestion, which raises the environmental effect of each of the subsidiary car journeys. This thesis deals with the quantitative problem of the supply and demand of car parking in urban areas through the prism of eco-design to make planning more sustainable. It proposes a simulation model which allows to study parking management plans, focusing on the technical functioning of the system and its interaction with road traffic. The first part proposes a comprehensive overview of the evolution of urban parking in public policies in France, followed by a theoretical analysis of the issues and the challenges raised by an eco-design approach of parking. A systemic exploration centered on its physical, functional and spatial dimensions is then given. Finally, a methodology for diagnosing parking practices at the level of an agglomeration based on a household travel survey is developed and illustrated by an application to the Île-de-France in 2010.The second part is devoted to the spatial modeling of the parking system. It begins with a review of specific literature. Then, we propose a static treatment of the interactions between parking and road traffic at the local level. A spatialized network assignment model (ParkCap model) is developed. It enables to model the joint choices of network route and parking lot and explicitly considers supply capacities constraints and the phenomenon of cruising for parking on the road network. The model’s performance is demonstrated by two numerical examples. Lastly, the structure of the computer simulator prototype is briefly presented. The third and final part provides an application of the model to the strategic planning of parking in the district of Cité Descartes. After a territorial diagnosis of the study area and its parking system, a simulation of the 2010 reference state is implemented. The application demonstrates the ability of the ParkCap tool to simulate a real network and to evaluate several parking management plans. We extend it to a prospective study of the parking system by 2030. By anticipating the urban transformations associated with the Greater Paris Express project, three contrasting scenarios of parking supply are compared and evaluated