## 4 Review of Studies Related to Sustainability in the Context of Multi-Modal Mobility
### Potential Future Research Directions
There are several future research directions following the study [71]: (1) investigating how other factors such as the ease of access to information influences the service access action; (2) collecting revealed preferences data for the framework; (3) extending to incorporate the en route opt-out behavior; (4) integrating the model into an ABM simulator for system-wide optimization. The impact of automated mobility-on-demand on urban mobility was investigated in the study [72] based on a multi-modal agent-based urban simulation platform. The study [72] raises several research issues: (1) studying how to apply automated mobility on-demand as a complementary service to mass transit, (2) optimization of fleet size and algorithms for re-balancing and ridesharing in automated mobility-on-demand, (3) studying policy implementations in automated mobility-on-demand, such as subsidizing automated mobility-on-demand services for connection to mass transit, (4) studying the effects of traffic control/management schemes such as transit signal priority or high-occupancy vehicle lanes and (5) redesign of mass transit services to work effectively with the availability of automated mobility-on-demand. In the study [73], a Petri Net model considering various sustainability factors was developed to provide decision support for sustainable household mobility choices in a multi-modal transport network. There are several future research directions following the study [73]: (1) developing hybrid models by combining the PNs with the predictive power of machine learning algorithms to enhance the model by predictive and adaptive capabilities of AI, (2) development of advanced stochastic frameworks for handling uncertainty and stochasticity in transportation systems based on stochastic PN models, (3) scalable hierarchical or modular PN models for large scale transportation networks and (4) application of PNs to model and optimize emerging complex multi-modal transportation systems technologies and paradigms. Urban policy design and implementation methods for sustainable urban mobility  
The issues under this category are to develop an innovative and collaborative decision-making framework to support integrated sustainable urban policy design for sustainable urban mobility. In the study [101], a multi-level decision-making framework was proposed to support integrated sustainable urban policy design for sustainable urban mobility and applied to the metropolitan region of Rhine-Ruhr for verification. Following the study [101], there are several future research directions: (1) comparison of the Rhine-Ruhr region with other megacities to build a future international benchmark on urban networks to co-produce and translate relevant knowledge across the continents and (2) systematically integrating emerging business models in mobility and energy sectors into a policy-making framework through advanced digital technologies. The issue to reconstruct a damaged urban system after earthquake to implement a more efficient urban sustainable transport system was addressed in the work [102]. The authors of [102] suggested several future research directions: (1) identification of the elements for integrating design models with sustainable networks, (2) analysis of key performance indices, and (3) investigation of policies for developing public transport in cities affected by disasters. Performance evaluation in multi-modal transport systems  
The study [107] probed into the problem about how to estimate long-term travel delay in multi-modal systems with different transport modes. As the study was based on the Shenzhen city, China, the authors of the study [107] suggested a future research direction to apply the proposed method to other cities to verify transferability of the results. The modal accessibility gap (MAG) refers to the spatial accessibility of different transport modes. The study [108] focused on measuring the modal accessibility gap (MAG) to assess the degree of low-energy travel mode in the city.