## 8 Discussion
### Opportunities and Challenges
The three research issues--\"impacts and substitution effects of micromobility\" [92; 93; 94], \"analysis of the potential impact caused by MaaS\" [84; 85; 86], and \"studying the relation between different transport modes and the impacts of transport modes\" [90; 91]--focus on the studies of the impacts caused by the emerging transport modes, their interaction and substitution effects. Understanding the impacts will be helpful for transportation planners to develop a smooth and feasible path in the migration and evolution processes to achieve sustainable multi-modal mobility. Analysis of the impacts caused by the emerging transport modes and their interaction with existing transport modes relies on the development of analysis or simulation methods/tools that can capture the dynamics and characteristics of real multi-modal MaaS systems. However, there are limitations in the existing analysis or simulation methods. For example, the spatial agent-based model used in [92] does not consider weather factors or psychological aspects such as comfort, perceptions of safety, and leisure travel. The analysis provided in [86] does not take into account transit frequency, vehicle capacity, or hidden costs associated with comfort during trips. These research gaps, due to the limitations of existing analysis and simulation methods, need to be closed. Simulation is one popular approach to studying MaaS and assessing their impacts on peoples' travel behavior, modal shifts in cities and bundling/pricing transportation services for MaaS. Different models have been developed to predict modal shifts to sustainable transportation options. These include agent-based models [150], statistical models [95], discrete choice models [116; 105; 60; 118] and system dynamic models. Statistical models and choice models cannot capture the dynamics behind users' choices and preferences, which are non-linear and evolving features that characterize mobility and behavioral change, and the interactions between agents and environments. System dynamics models is a top-down approach that model the aggregate system instead of disaggregated parts of a system. Agent-based models overcome the shortcomings of statistical models, discrete choice models and system dynamic models as they are a bottom-up approach capable of capture heterogeneous mobility decisions made by individual agents and their interactions. As the agent-based approach allows for the modelling of flexibility, proactivity and negotiation capabilities of entities in collaborative transportation systems such as MaaS, agent-based modelling (ABM) is the mainstream technique for modeling and analyzing the mobility transition [150]. The study [151] indicates that MATSim, an open-source framework for large-scale agent-based transport simulations, is one popular tool used to estimate the transport demand. MATSim supports the co-evolutionary algorithm, which performs optimization within an agent's set of plan based on the scoring functions of individual agents and is different from evolutionary algorithms that aims to find the global optimal solution. MATSim can be extended with other modeling framework such as BEAM [152], a modeling framework for behavior, energy, autonomy and mobility, to specify multiple mode choices. The MaaS in cities with multi-transport modes can be modeled as \"System of Systems\" design problems [124]. Such a \"System of Systems\" relies on the use of two or more simulators to model and simulate the behaviors in multi-modal MaaS. In [82], the concept of co-simulation was proposed to study the interaction of the demand model provided by the MATSim simulator and a fleet simulation model provided by the IMSim simulator. Despite the studies mentioned above, research gaps remain in studying systematic approaches to combining optimization methods with individual simulators and in building an overall model for multi-modal MaaS through the co-simulation of multiple optimization-based simulators.