Dynamic and Pareto-improving swapping of vehicles to enhance integrated and modular mobility services

Efficiency of many mobility services depends on effective matching of service vehicles and customers over time and space. New technologies, such as those related to service integrators and modularized autonomous vehicle platforms (i.e., chassis with built-in power and control systems), further allow pooling of vehicles from traditionally separate service operators to flexibly serve multiple types of customers (e.g., passengers and freight). Dynamic matching systems, if not carefully managed, suffer from the so-called wild goose chase (WGC) phenomenon, which describes an inefficient system equilibrium with a large number of vehicles trapped in unproductive deadheading. This paper proposes a new dynamic vehicle swap strategy that can be used to enhance system efficiency by reducing the expected waiting/deadheading time. This strategy can not only mitigate the WGC and reduce the needed fleet size, but also ensure that the vehicle swap achieves a Pareto improvement for each involved participant. Approximate analytic formulas are derived from a series of differential equations and spatial probability models to estimate the expected system performance in the steady state. This system of nonlinear equations can be solved numerically to yield the equilibrium points and the associated system performance metrics. Agent-based simulations are used to verify the accuracy of the derived formulas, and to demonstrate the effectiveness of the proposed dynamic and Pareto-improving swap strategy in a variety of application contexts (e.g., taxi, service integrator, or pooled modular chassis).