Planning and Dynamic Management of Autonomous Modular Mobility Services

<p>This project aimed to address the challenges faced by on-demand mobility operators in understanding and addressing spatiotemporal random bipartite matching problems (ST-RBMPs). At the planning level, we developed analytical models to estimate the expected system performance in a static RBMP. At the operational level, we designed solution algorithms to improve the overall service efficiency in ST-RBMPs with different types of supply arrivals. Although our main focus was on the application of on-demand mobility services, these models can also be applied to other contexts such as resource allocation, target detection, etc. This project aimed to address the following research objectives:</p>

<p>1. Propose an analytical model with closed-form formulas (without statistical curve fitting) that estimate the expectation of the optimal matching distance for static RBMP, where the bipartite vertices are distributed randomly over a discrete network. These formulas can be incorporated into queuing and optimization models to identify the best operational strategies in on-demand mobility systems with closed- or open-loop resource arrivals. It helps determine the optimal decision timing for whether newly arriving customers should be matched instantly or pooled into a batch for matching.</p>

<p>2. For ST-RBMPs with closed-loop resources, where arriving customers shall be matched instantly, the objective is to propose a Pareto-improving strategy that allows matched vertices to be swapped among candidates with improved matching distances as the system evolves. This strategy could enhance system efficiency by reducing the overall expected matching distance and mitigating the so-called Wild Goose Chasing (WGC) phenomenon. Approximate analytic formulas can be derived from a series of differential equations and spatial probability models to estimate the expected system performance in the steady state.</p>