## 4 Review of Studies Related to Sustainability in the Context of Multi-Modal Mobility
### Potential Future Research Directions  
The above review of the recent papers related to shared mobility and ridesharing within the context of multi-modal mobility highlights several research issues and questions requiring further investigation. These research issues or questions include (i) modeling, simulation and planning in multi-modal transport networks, (ii) leveraging the benefits of shared mobility to enhance multi-modal transport and (iii) studying the relation between different transport modes and the impacts of transport modes. These potential future research directions are summarized in this subsection. 1. Modeling, simulation and planning in multi-modal transport networks  
Although the problem to transport a set of travelers in a travel group called a joint travel problem has been addressed in [74], there are several interesting directions for future research related to [74], including (1) studying the use of personalized preferences and choice making mechanisms to achieve accurate synchronizations among the individuals, (2) developing location selection models taking into account space-time constraints, coupling constraints and vehicle capacity constraints to reduce the run-time complexities, (3) extending the study to deal with the joint travel problem with multi-modal, multi-person and multi-activity, (4) development of techniques to speed up the solution finding process and (5) extending the study to deal with the joint travel problem under uncertainty. Evaluation of the operation of transport systems with different transport modes, including private cars, walking, public transport, ridesourcing, ridesharing-as-driver, and ridesharing-as-rider, has been studied in [75]. However, the model used in the study [75] is not general enough to deal with situations in the real-world environment. Including other players in the formulation, considering energy supply market players of electric vehicles in the model and inclusion of stochastic aspects in the model are interesting research directions. In addition, the ridesourcing service is defined over the links in the proposed model. Extension to deal with origin-destination requirement is required to solve a problem in the real world. In [76], a multi-modal route choice model with ridesharing and public transit was developed based on single-origin-destination pair network and was expressed as an equivalent complementarity problem. There are several future research directions following the study [76]: (1) consideration of departure time choice, (2) extension of the proposed model to multi-user classes with different values of time or ridesharing preferences to study the influence on the equilibrium flow patterns and (3) studying the design of traffic policies and their effects on the ridesharing behavior. Although the study [77] showed that the scheme to provide travel alternatives across multiple modes, such as P2P ridesharing, transit, city bike-sharing and walking, can expand the coverage of public transit, it only considers travel demand from personal vehicles. Consideration of transit demand is an interesting issue to study the potential improvements and mode shift. Other behavior models, e.g., random utility models could be studied and implemented in the future research. To study the effects of pricing, transfer and waiting time on riders' choice behavior, sensitivity analysis is required. The work [78] developed based on an aggregate traffic representation to study how time-dependent travelers' choices and traffic conditions evolve from day to day was limited. There are several directions for further research: (1) extension to account for more complex networks and traffic models, (2) studying the feasibility to replace the aggregate traffic model with a detailed link-node model, or cell-transmission model, or link-transmission model, (3) extension to adopt alternative multi-modal MFD to incorporate flow interaction between car traffic and transit vehicles and (4) extension to stochastic MFD.