## 8 Discussion
### Opportunities and Challenges
The framework proposed in [134] includes the general optimization formulation of the design models detailed in [137], a decision support system (DSS) platform and sustainable objectives and goals. The work of [137] reports a methodology that consider actors such as operators, companies, users, citizens and system manager/planner in MaaS to specify transport system models for estimating the effects of decision makers' actions on MaaS. The methodology includes a framework to support S-MaaS policies definition and transport system models for services design. The optimization model specified in [137] includes the objective function, control variables, internal variables, external/technical constraints and behavioral constraints. The DSS platform refers to models, algorithms and methods that support stakeholders such as public authorities, operators, companies and users. In addition to the optimization frameworks mentioned above, modeling of travel demand and its interaction with supply is an important part in the development of a complete methodology. The study reported in [136] focuses on travel demand modelling and analysis based on the transportation system models. Different means for collecting and estimating the travel demand may be estimated by means of disaggregated sample, surveys and aggregate surveys such as characteristics of the area and traffic flows. The dynamic model to capture the interaction between demand and supply in MaaS with ICT has been studied in [138] to achieve goal of sustainable mobility. In [138], the authors analyzed the goals and targets of Agenda 2030 and highlighted those for which MaaS can contribute to the pursuit. They formulated a dynamic model based on transport system models which includes the formalization of emerging ICT technologies in the demand model, the supply model and the interaction model. The authors applied the proposed transport system models to quantitatively evaluate the MaaS development policies with respect to the targets of Agenda 2030. In addition to the above studies, a project that focused on the development of methodologies for the evaluation of MaaS, conduction of pilot surveys on samples of potential users, and analysis of results to assess the potential of MaaS and its impacts on mobility was reported in [139]. Although the studies mentioned above provide systematic methods to pave the way for the development of sustainable MaaS, a research gap remains in applying the proposed methods to solve real-world multi-modal mobility optimization problems, as this is a non-trivial task. For example, to apply the optimization model specified in [137], the objective function, control variables, internal variables, external/technical constraints and behavioral constraints that need to be defined according to the target application scenario. The complexity of single modal shared mobility problems are already very complex [140,141]. The complexity to develop solution algorithms for the optimization sustainable multi-modal MaaS is one challenge. Other issues such as scalability and agility of solution algorithms pose additional challenges to develop solvers for the optimization and planning of sustainable multi-modal MaaS. In Table 15, the number of papers addressing the research issue \"Effective approaches to bundling and pricing transportation services for MaaS\" is the second highest among all research issues. This shows that bundling [118] and pricing [119; 122] transportation services have received prominent attention. The number of papers addressing research the issue \"Systematic approaches to achieving different levels of integration\" is also high. An interesting future research direction to close the research gap related to this issue is to study how to achieve different levels of integration through developing proper bundling and pricing schemes to incentivize the potential MaaS users and service providers. Pricing in emerging single modal mobility services has been extensively studied.