## 9 Conclusions and Future Directions  
In this paper, we described the conceptual design and implementation of an eco-friendly multi-modal route guidance (EMRG) system. This system simulates and combines seven modes of transport, namely, roads, buses, trolleybuses, trans, metros, bicycles, and walking, to promote sustainable travel behavior in urban cities where traffic congestions and carbon emissions are among the most prevalent challenges. Our proposed intelligent transport system employs multiple software agents to represent and realize the simulation of the infrastructure and traffic conditions of an urban environment, and accordingly recommends two types of route solutions. The first suggested route is fast and tries to avoid congested road segments, thus balancing the traffic stream in the transport network and reducing the journey time. However, the second suggested route is eco-friendly for it endeavors to mix public transport, cycling, and walking, thus reducing the carbon footprint of trips. Moreover, our proposed system proved to be scalable in terms of accommodating new agents. Implementing and scaling the number of agents could also be performed with ease. Moreover, more modalities of transportation could be added in the infrastructure. An initial pilot study was carried out, and the results showed that the proposed ITS can provide useful route recommendations, which are based on the live traffic and public transport information provided to the system. The pilot study demonstrated the scalability and adaptability of the ITS in different deployments, irrespective of their differences in the traffic infrastructure. Furthermore, we have undertaken two trial studies in two representative European cities, namely Nottingham (United Kingdom) and Sofia (Bulgaria), to demonstrate the usefulness and practicality of our approach. Ninety-two real commuters took part in the experiments and gave their impressions about the usability and effectiveness of our multimodal traffic management system. Overall, 85% of the selected trips were green routes, which demonstrates the effectiveness of the system in promoting green travel. Our future works aim to augment the ITS recommender system with computational intelligence (e.g., machine learning and data analytics) to, firstly, predict traffic jams and incidents based on the commuting patterns and, secondly, suggest multimodal routes that evade congestions and delays based on these predictions. In addition to travel time and carbon emissions, we intend to consider other traffic metrics like traffic delays, queues, and accidents for route calculations. We also plan to explore the societal impact of our route recommender system with its emphasis on eco-friendliness to prove how such a system can really influence and encourage commuters to uptake commuting decisions that consider environmental factors. Furthermore, we envision to introduce a commuter profiling mechanism that could aid them in decision making based on various factors related to their internal and external constraints, past experiences, habits, preferences, and values, among others. We intend to compare the journey times of the predicted routes by our ITS recommender system to existing route optimization and planning systems. Conceptualization, A.N., N.M., and E.P. ; Formal analysis, A.N. and A.T.; Funding acquisition, N.M. and E.P. ; Methodology, A.N. ; Resources, A.N. and A.A.; Software, A.N. and J.A. ; Validation, A.N., N.M., E.P., and J.A. ; Visualization, A.A. and J.A. ; Writing--original draft, A.N., A.T., J.A., and N.M.; Writing--review & editing, A.N. and A.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Union, under grant number 288205, and the APC was funded by the authors. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data sharing not applicable.