Mobility-on-demand (MOD) services have the potential to significantly improve the adaptiveness and recovery of urban systems, in the wake of disruptive events. But there lacks a comprehensive review on using MOD services for such purposes in addition to serving regular travel demand. This paper presents a review that suggests a noticeable increase within recent years on this topic across four main areas - resilient MOD services, novel usage of MOD services for improving infrastructure and community resilience, empirical impact evaluation, and enabling and augmenting technologies. The review shows that MOD services have been utilized to support anomaly detection, essential supply delivery, evacuation and rescue, on-site medical care, power grid stabilization, transit service substitution during downtime, and infrastructure and equipment repair. Such a versatility suggests a comprehensive assessment framework and modeling methodologies for evaluating system design alternatives that simultaneously serve different purposes. The review also reveals that integrating suitable technologies, business models, and long-term planning efforts offers significant synergistic benefits.

The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.