School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Incentive Mechanisms for Collaborative Routing Factoring Individual Heterogeneties

By Chaojie Wang

Advisor:

Dr. Srinivas Peeta (CEE)

Committee Members:  Dr. Patricia Mokhtarian (CEE), Dr. Jorge Laval (CEE), Dr. Omar Isaac Asensio (Public Policy),
Dr. Samuel Labi (Purdue University), and Dr. Lili Du (University of Florida)

Date and Time:  Aug 2nd, 2023 at 1:30 PM

Location: SEB 122 & Zoom Meeting: 982 1633 3905 (Passcode 281724)

Behavioral interventions have been widely examined and employed in transportation systems as demand-side approaches to alleviate traffic congestion. In contrast to traffic tolls, incentive mechanisms are garnering interest, as they do not impose additional costs on travelers and are therefore more politically acceptable. Emerging communication technologies also provide incentive mechanisms with the potential to influence routing behaviors through on-board units in connected and autonomous vehicles (CAVs) and smartphones of human drivers. Nevertheless, several challenges may undermine their effectiveness and deployability, including (i) the inability to account for individual heterogeneities, (ii) the assumption of behavioral obligation that overlooks the participation willingness of CAVs and human-driven vehicles (HDVs), (iii) the disregard for budget constraints that compromise the sustainability of the mechanisms, and (iv) the absence of efficient decentralized solution algorithms for large-scale implementations.
 
        The dissertation commences with the formulation of incentive mechanisms within the most manageable context, a fully CAV environment. Exploiting the predictable route choice behavior of CAVs, a hierarchical incentive-based decentralized routing approach is proposed to decouple route optimization and incentive optimization, thereby enabling real-time personalized incentive calculations. The proposed incentive mechanism yields individual rationality, budget balance, and incentive compatibility. Subsequently, the second study formulates a personalized incentive mechanism for mixed traffic flow of CAVs and HDVs, aiming to enhance the system performance during the transitional phase. By assumption, HDVs exhibit more irrational and unpredictable behavior, and their interactions with CAVs render the modeling process increasingly complex. To computationally facilitate this incentive mechanism, collaborative routing is proposed, allowing individual CAVs and HDVs to negotiate tentative routing preferences and request necessary personalized incentives until a consensus is reached. A crucial insight from this study reveals that relying solely on monetary incentives might not be budget sustainable when HDVs remain predominant. Consequently, the third study extends the collaborative routing strategy by integrating an incentive bundle optimization model, capable of generating personalized bundles comprising various non-monetary incentives to effectively and sustainably influence human drivers. The fourth study investigates the potential of personalized incentive mechanisms in enhancing equity within transportation systems. Three novel equity dimensions—accessibility equity, inclusion equity, and utility equity—are introduced and incorporated into the model. Lastly, the fifth study addresses the privacy concerns associated with personalized incentive mechanisms by employing secure multiparty computation (MPC) and blockchain technologies.