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This paper investigates the impact of COVID-19 on travel behavior in different socio-economic segments in the USA using integrated mobile device location data over the period 1 Jan 2020 ~ 20 Apr 2021. A fixed-effect panel regression model is estimated to statistically identify the relationship between COVID monitoring measures and travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH in different socio-economic segments. We find that as exposure to COVID increases, the number of trips and traveling miles starts to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and may never return to pre-COVID level. The findings have implications for understanding the heterogeneous mobility response of individuals in different socio-economic segments to various COVID waves, and thus can provide insights into the recovery of travel behavior.

In the Mobility-as-a-Service (MaaS) systems under government contracting, this thesis proposes innovative auction-based MaaS mechanisms where users arrive dynamically and compete for mobility resources by bidding for mode-agnostic mobility resources based on their willingness to pay and preferences on service experience. This thesis takes the perspective of a MaaS regulator, which aims to maximize social welfare by allocating mobility resources to users. This thesis proposes two MaaS mechanisms that allow users to either pay for the immediate use of mobility service, Pay-as-You-Go (PAYG), or subscribe to mobility service packages, Pay-as-a-Package (PAAP). This thesis casts the proposed mechanisms as online mobility resource allocation problems to accommodate user bids based on available mobility resources. It is shown that the proposed PAYG mechanism is incentive-compatible, individually rational, and budget-balanced. This thesis proposes two polynomial-time online algorithms for both mechanisms and derives its corresponding competitive ratio relative to an offline optimization problem. The thesis also explores rolling horizon configurations with varying look-ahead policies to implement the proposed mechanisms. In the MaaS systems under economic deregulation, a MaaS platform can be viewed as a two-sided market where travelers and transportation service providers (TSPs) are two groups of interacting agents. This thesis proposes an optimization framework for the regulation of two-sided MaaS markets, uses an auction mechanism to model the behavior of travelers and TSPs, and casts this problem as a single-leader multi-follower game (SLMFG) where the leader is the MaaS regulator and two groups of follower problems represent the travelers and the TSPs. The MaaS regulator aims to maximize its profits by optimizing service prices and resource allocation. In response, travelers (resp. TSPs) adjust their participation level in the MaaS platform to minimize their travel costs (resp. maximize their profits). This thesis analyzes network effects in the two-sided MaaS market and formulates SLMFGs without/with network effects. This thesis provides constraint qualifications for the proposed SLMFGs and develops customized branch-and-bound algorithms based on strong-duality reformulations to solve SLMFGs. Considering the MaaS ecosystems with multi-disciplinary collaborators, this thesis models a MaaS ecosystem providing mobility services and instant delivery services by sharing the same transport system. This thesis derives integrated mobility and delivery user equilibrium (IMDUE) in the MaaS ecosystem and proposes a bilateral surcharge and reward scheme (BSRS) to manage the integrated mobility and delivery demand in different scenarios. This thesis proposes a bilevel model to optimize the proposed BSRS, where the upper-level problem aims to minimize the total system equilibrium costs of mobility and delivery users and the lower-level problem is the derived IMDUE under BSRS. After exploring the properties of the BSRS, a trial-and-error solution algorithm is proposed to solve the proposed bilevel optimization problem based on the properties of the optimal solutions under BSRS. Overall, this thesis proposes a unified framework and tractable optimization methodologies for the innovative MaaS paradigm, exploits the potentialities of MaaS systems to evaluate futuristic transport scenarios, and provides meaningful managerial insights for the regulation of MaaS systems.