The thesis focuses on economic analysis and modeling of urban public transportation. It includes studies on uncongested transit service under monopoly, congested transit service under monopoly and transit service differentiation under oligopoly.

For uncongested transit service, we present a model of public transit service under monopoly when potential users differ in their willingness to pay and value of time. The transit operator chooses service frequency and the fare to maximize a weighted sum of profit and consumers’ surplus. Profit-maximizing and social-surplus-maximizing frequency decisions are compared using a unified framework that includes results of previous studies as special cases. The prevalence of the Mohring Effect and the need for subsidization are investigated. F...[ Read more ]

The thesis focuses on economic analysis and modeling of urban public transportation. It includes studies on uncongested transit service under monopoly, congested transit service under monopoly and transit service differentiation under oligopoly.

For uncongested transit service, we present a model of public transit service under monopoly when potential users differ in their willingness to pay and value of time. The transit operator chooses service frequency and the fare to maximize a weighted sum of profit and consumers’ surplus. Profit-maximizing and social-surplus-maximizing frequency decisions are compared using a unified framework that includes results of previous studies as special cases. The prevalence of the Mohring Effect and the need for subsidization are investigated. Four types of regulatory policies are then considered: fare regulation, frequency regulation, goal or objective function regulation, and fiscal regulation whereby the operator receives a subsidy based on consumers’ surplus or demand. A numerical example is used to assess the relative efficiency of the regulatory regimes, and illustrate how the solutions depend on the joint distribution of willingness to pay and value of time.

For congested transit service, we study a model of congested transit service under monopoly when potential users differ in their characteristics. Given travel time delays and crowding externalities, general user heterogeneity is characterized in a three-dimensional space of value of time, value of crowding, and willingness to pay. A unique user demand equilibrium is shown to exist under general service conditions. The operator chooses the fare and service frequency to maximize a weighted sum of profit and consumers’ surplus. The socially optimal fare consists of marginal operating cost, external user congestion cost, and a nonnegative shadow price on the vehicular capacity constraint which may or may not bind. A general cost-recovery formula is also derived. Two methods for optimal design capacity are proposed that differ as to whether fare and frequency are exogenous or set conditional on capacity choice. Two numerical examples, without and with crowding, are presented to illustrate the theoretical results.

For transit service differentiation, we consider mixed uncongested transit oligopoly and analyze strategic interactions among passengers, operators and the regulator. A number of operators provide users with vertically differentiated transit services. Heterogeneous users choose transit based on their willingness to pay and minimize the generalized costs. The active transit market is segmented and illustrated in the two-dimensional value of time and willingness to pay space. We show that the equilibrium number of operators is upper bounded. We explain that an operator who sets too high fare and assigns sufficient weight to consumers’ surplus, is not sustainable under market equilibrium with fare optimization. We prove that the social surplus maximum number of operators is one under certain conditions. We formulate the active transit market problem under market constraints, and develop a randomized market boundary algorithm. This algorithm is a type of random walk Monte Carlo method. Solution convergence in probability is proved. Numerical examples are conducted to test the findings.