Stabilizing the Psychological Dynamics of People in a Crowd

This thesis investigates the use of control theory as a means to study and ultimately control the psychological dynamics of people in a crowd. Gustav LeBon's suggestibility theory, a well-known account of collective behaviour, is used to develop a discrete-time nonlinear model of psychological crowd behavior that, consistent with suggestibility theory, is open-loop unstable. As a first attempt to stabilize the dynamics, linear observer-based output-feedback techniques and a collection of simple nonlinear control strategies are pursued. The poor performance afforded by these schemes motivates an agent-oriented control strategy in which authoritative figures, termed control agents, are interspersed within the crowd and, similar to the technique of feedback linearization, use knowledge of the system dynamics to issue signals that propagate through the crowd to drive specific components of the state to zero. It is shown that if these states are chosen judiciously then it follows that a collection of other state signals are, themselves, zero. This realization is used to develop a stability result for a simple crowd structure and this result is, in turn, used as a template to develop similar results for crowds of greater complexity. Simulations are used to verify the functionality of the reported schemes and the advantages of using multiple control agents, instead of a single control agent, are emphasized. While the mathematical study of complex social phenomena, including crowds, is prefixed by an assortment of unique challenges, the main conclusion of this thesis is that control theory is a potentially powerful framework to study the underlying dynamics at play in such systems

On the Statistics and Predictability of Go-Arounds

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.United States. National Aeronautics and Space Administration (Grant NNX08AY52A)

On the Statistics and Predictability of Go-Arounds

This paper takes an empirical approach to identify operational factors at busy airports that may predate go-around maneuvers. Using four years of data from San Francisco International Airport, we begin our investigation with a statistical approach to investigate which features of airborne, ground operations (e.g., number of inbound aircraft, number of aircraft taxiing from gate, etc.) or weather are most likely to fluctuate, relative to nominal operations, in the minutes immediately preceding a missed approach. We analyze these findings both in terms of their implication on current airport operations and discuss how the antecedent factors may affect NextGen. Finally, as a means to assist air traffic controllers, we draw upon techniques from the machine learning community to develop a preliminary alert system for go-around prediction.Comment: 10 pages, 14 figures, Submitted to USA/Europe ATM Seminar 201

Tactical strategies to search for scarce resources

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, February 2015.Cataloged from PDF version of thesis. "October 2014."Includes bibliographical references (pages 143-149).This thesis investigates search scenarios in which multiple mobile, self-interested agents, cows in our case, compete to capture targets. The problems considered in this thesis address search strategies that reflect (i) the need to efficiently search for targets given a prior on their location, and (ii) an awareness that the environment in which searching takes place contains other self-interested agents. Surprisingly, problems that feature these elements are largely under-represented in the literature. Granted, the scenarios of interest inherit the challenges and complexities of search theory and game theory alike. Undeterred, this thesis makes a contribution by considering competitive search problems that feature a modest number of agents and take place in simple environments. These restrictions permit an in-depth analysis of the decision-making involved, while preserving interesting options for strategic play. In studying these problems, we report a number of fundamental competitive search game results and, in so doing, begin to populate a toolbox of techniques and results useful for tackling more scenarios. The thesis begins by introducing a collection of problems that fit within the competitive search game framework. We use the example of taxi systems, in which drivers compete to find passengers and garner fares, as a motivational example throughout. Owing to connections with a well-known problem, called the Cow-Path Problem, the agents of interest, which could represent taxis or robots depending on the scenario, will be referred to as cows. To begin, we first consider a one-sided search problem in which a hungry cow, left to her own devices, tries to efficiently find a patch of clover located on a ring. Subsequently, we consider a game in which two cows, guided only by limited prior information, compete to capture a target. We begin by considering a version in which each cow can turn at most once and show this game admits an equilibrium. A dynamic-programming-based approach is then used to extend the result to games featuring at most a finite number of turns. Subsequent chapters consider games that add one or more elements to this basic construct. We consider games where one cow has additional information on the target's location, and games where targets arrive dynamically. For a number of these variants, we characterize equilibrium search strategies. In settings where this proves overly difficult, we characterize search strategies that provide performance within a known factor of the utility that would be achieved in an equilibrium. The thesis closes by highlighting the key ideas discussed and outlining directions of future research.by Kevin Spieser.Ph. D

On the transfer time complexity of cooperative vehicle routing

Motivated by next-generation air transportation systems, this paper investigates the relationship between traffic volume and congestion in a multi-agent system, assuming that the agents can communicate their intentions with one another. In particular, we consider n independent mobile agents, each assigned an origin and a destination point, and study how the minimum time necessary to safely transfer all agents from their origin to their destination scales with the number of agents n. We provide an algorithm for which the transfer time scales logarithmically in n. This is an improvement over previous results that rely on more conservative conflict models because the agents do not leverage inter-agent cooperation to the same degree, resulting in transfer times that scale as √n

On the transfer time complexity of cooperative vehicle routing

Motivated by next-generation air transportation systems, this paper investigates the relationship between traffic volume and congestion in a multi-agent system, assuming that the agents can communicate their intentions with one another. In particular, we consider n independent mobile agents, each assigned an origin and a destination point, and study how the minimum time necessary to safely transfer all agents from their origin to their destination scales with the number of agents n. We provide an algorithm for which the transfer time scales logarithmically in n. This is an improvement over previous results that rely on more conservative conflict models because the agents do not leverage inter-agent cooperation to the same degree, resulting in transfer times that scale as √n [square root of n].United States. National Aeronautics and Space Administration. (Grant NNX08AY52A

NSC148286

The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of ion in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.Singapore. National Research FoundationSingapore-MIT Alliance for Research and Technology Center (Future Urban Mobility SMART IRG program