Incentive Mechanisms for Collaborative Routing Factoring Individual Heterogeneities
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Wang, Chaojie
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Abstract
Behavioral interventions have been widely examined and employed in transportation systems to alleviate traffic congestion. In contrast to penalty-based solutions such as 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 used by human drivers. Nevertheless, several challenges may undermine their effectiveness and practical deployment, including: (i) the need 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 need to factor budget constraints that could otherwise compromise the sustainability of the mechanism, and (iv) the absence of efficient decentralized solution algorithms for large-scale implementations.
To address the above challenges, this dissertation aims to create personalized incentive mechanisms that influence individual route choices without assuming behavioral compliance. It leverages efficient decentralized computational frameworks enabled by emerging connectivity technologies to ensure computational feasibility. Further, it seeks to find an optimal balance within the associated trilemma that includes the incentive mechanism's effectiveness in improving system performance, the users' willingness to participate, and the budget sustainability for traffic operators, under diverse real-world scenarios.
The dissertation first develops a personalized incentive mechanism in a pure CAV environment. CAVs may exhibit willingness to participate even under negative incentives, provided their utility gains from participation outweigh the costs. Exploiting the flexible participation criteria of CAVs, a hierarchical incentive-based cooperative routing approach is proposed to decouple route optimization and incentive optimization, thereby enabling the real-time computation of personalized incentives. The associated incentive mechanism yields individual rationality, budget balance, and incentive compatibility. Next, the dissertation formulates a personalized incentive mechanism for mixed traffic flow of CAVs and HDVs, aiming to enhance the system performance during the transition to the fully autonomous future. Unlike CAVs, HDVs are posited to require strictly non-negative incentives due to the cognitive load involved in processing the implications of negative incentives. To computationally facilitate the corresponding incentive mechanisms, collaborative routing is proposed, allowing CAV groups and individual HDVs to negotiate tentative routing preferences and CAV flows and request necessary incentives until a consensus is reached. A crucial insight is that relying solely on cash incentives may not be sustainable from the budget perspective when CAVs are sparse in the traffic flow. Consequently, the third dissertation topic extends the collaborative routing strategy by integrating an incentive bundle optimization model, capable of generating personalized bundles consisting of various non-cash incentive types to influence human drivers’ routing behavior effectively and sustainably. The fourth topic investigates the potential of personalized incentive mechanisms to enhance equity in transportation systems. It defines and proposes three types of equity: (i) accessibility equity, implying equal access to employment, services, and educational opportunities for all users; (ii) inclusion equity, aimed at generating routing preferences and incentives that do not disproportionately benefit some users over others; and (iii) utility equity, which seeks envy-free solutions whereby no user perceives that the route options and incentives allocated to others are unduly favorable. The associated incentive mechanism seeks an optimal balance within the trilemma, encapsulating system objectives that encompass both efficiency and equity considerations. Finally, the fifth topic addresses privacy concerns associated with personalized incentive mechanisms by employing secure multiparty computation (MPC) and blockchain technologies.
In summary, by investigating the potential of personalized incentive mechanisms under various dimensions, this dissertation provides a pragmatic paradigm and framework for personalized incentive mechanism design to influence route choices to enhance traffic network performance and manage congestion.
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Date
2024-04-30
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Dissertation