Doctor of Philosophy

dissertationTraffic congestion occurs because the available capacity cannot serve the desired demand on a portion of the roadway at a particular time. Major sources of congestion include recurring bottlenecks, incidents, work zones, inclement weather, poor signal timing, and day-to-day fluctuations in normal traffic demand. This dissertation addresses a series of critical and challenging issues in evaluating the benefits of Advanced Traveler Information Strategies under different uncertainty modeling approaches are integrated in this dissertation, namely: mathematical programming, dynamic simulation and analytical approximation. The proposed models aim to 1) represent static-state network user equilibrium conditions, knowledge quality and accessibility of traveler information systems under both stochastic capacity and stochastic demand distributions; 2) characterize day-to-day learning behavior with different information groups under stochastic capacity and 3) quantify travel time variability from stochastic capacity distribution functions on critical bottlenecks. First, a nonlinear optimization-based conceptual framework is proposed for incorporating stochastic capacity, stochastic demand, travel time performance functions and varying degrees of traveler knowledge in an advanced traveler information provision environment. This method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions across different days. Using a gap function framework, two mathematical programming models are further formulated to describe the route choice behavior of the perfect information and expected travel time user classes under stochastic day-dependent travel time. This dissertation also presents adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability. To account for different levels of information availability and cognitive limitations of individual travelers, a set of "bounded rationality" rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. In addition, this dissertation investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. The proposed models provide theoretically rigorous and practically usefully tools to understand the causes of travel time unreliability and evaluate the system-wide benefit of reducing demand and capacity variability

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Modeling rationality to control self-organization of crowds: An environmental approach

In this paper we propose a classification of crowd models in built environments based on the assumed pedestrian ability to foresee the movements of other walkers. At the same time, we introduce a new family of macroscopic models, which make it possible to tune the degree of predictiveness (i.e., rationality) of the individuals. By means of these models we describe both the natural behavior of pedestrians, i.e., their expected behavior according to their real limited predictive ability, and a target behavior, i.e., a particularly efficient behavior one would like them to assume (for, e.g., logistic or safety reasons). Then we tackle a challenging shape optimization problem, which consists in controlling the environment in such a way that the natural behavior is as close as possible to the target one, thereby inducing pedestrians to behave more rationally than what they would naturally do. We present numerical tests which elucidate the role of rational/predictive abilities and show some promising results about the shape optimization problem

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications

This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model

Multi-agent network games with applications in smart electric mobility

The growing complexity and globalization of modern society brought to light novel problems and challenges for researchers that aim to model real-life phenomena. Nowadays communities and even single individuals cannot be considered as a closed system, since one's actions create a ripple effect that ends up influencing the action of others. Therefore, the study of decision-making processes over networks became a pivotal topic in the research community. The possible applications are virtually endless and span into many different fields. Two of the most relevant examples are smart mobility and energy management in highly populated cities, where a collection of (partially) noncooperative individuals interact over a network trying to reach an efficient equilibrium point, in the sense of Nash, and share limited resources due to the environment in which they operate. In this work, we approach these problems through the lens of game theory. We use different declinations of this powerful mathematical tool to study several aspects of these themes. We design decentralized iterative algorithms solving generalized network games that generate behavioral rules for the players that, if followed, ensure global convergence. Then, we question the classical assumption of perfect players’ rationality by introducing novel dynamics to model partial rationality and analyzing their properties. We conclude by focusing on the design of optimal policies to regulate smart mobility and energy management. In this case, we create a detailed and more realistic description of the problem and use a nudging mechanism, implemented by means of a semi-decentralized algorithm, to align the users' behavior with the one desired by the policymaker