Modelling and Optimization of Smart Mobility Systems with Agent Envy as a Paradigm for Fairness and Behavior
Smart Urban Mobility in the future demands a paradigm shift. Transportation supply needs to be designed to incorporate individual-level preferences in an era of readily-available information about other users and network performance. It is, therefore, reasonable to expect that an individual would have information to compare his/her transportation allocation with other users. For individuals having the same goal (e.g., the shortest path to the destination from the same departure location and time), the peer to peer comparison may induce ‘envy’ if the user perceives his/her assigned travel option to be worse than that of his/her peers.
In turn, a user may adjust his/her travel options until he/she does not feel envy. This concept is an extension of the well-known travel behavior assumption called “User Equilibrium”. Existing behavior models, however, do not allow users to compare their allocations with others on an individual basis. Furthermore, it is assumed that users have perfect information about their own alternative and all users are homogeneous. A smart mobility system of the future may also include users who are not human but machines such as logistics, an autonomous vehicle that may have programmed behavior, and thus they too can be considered “agents” in our analysis.
This dissertation is dedicated to modeling a smart mobility system which accounts for individual level of allocation. Mobility systems that include connected, autonomous, and subscribed components to various extents will all qualify as smart systems in this context. More specifically, we focus on the optimization of the allocation problem to achieve both system-wide efficiency and minimum envy among individuals. We consider envy to be an important allocation aspect in the transportation system. Maximizing the efficiency of a system necessarily brings about some level of unfairness where some users (or agents) are allocated to inferior alternatives. When agents having superior alternatives can compensate the envy of groups having inferior alternatives, an envy-free state can be achieved—which can be shown to be Pareto efficient state. Using a combination of pricing and incentives, we propose an optimization model to arrive at this new equilibrium.
This research has significant contributions in that the proposed model provides a framework to combine system-wide objectives with individual users’ utility objectives. Furthermore, we consider user heterogeneity, which has not been researched in the general area of transportation assignment. The proposed optimization model can be applied to pricing strategies both for commercial and public agencies, who have real-time information about customer characteristics and system performance.
Numerical results from running our optimization on both illustrative and real networks show that the proposed model converges to both envy-free and system optimum states with appropriate allocation and pricing schemes. Our findings show that the proposed smart mobility system technically works efficiently without governmental subsidy since the budget-balance mechanism trades off credits among users. In addition, the level of user heterogeneity affects the amount of credits charged or disbursed.