## 8 Implications and Limitations  
The early results show that our multimodal solution is effective, to a certain extent, in changing travel behavior. This finding confirms previous claims that travel information, such as a bus timetable, impacts commuting decision making and travel behavior [15]. However, our research lacked a strong comparative experiment against existing systems. Table 1 lists some of the potential competitor frameworks and platforms; however, the comparison is not straight forward. For example, the authors in [53] presented a centralized architecture that aims to reduce journey times focusing on autonomous cars. Their solution balances the traffic flow of the city by considering the current and futuristic behavior of cars. On the positive note, the study simulates and validates the model in a European city using real traffic data and shows a reduction in travel times (up to 8%). On the other hand, it does not combine different modes of transport. In our view, our system cannot be directly compared to, e.g., the model presented in [53] because of obvious differences, including (1) our focus was on the scalability of the architecture to accommodate big metropolitan cities, (2) the combination of multiple types of transport, (3) the inflection of commuting behavior change, and (4) the estimation of CO\({}_{2}\) emissions. Although our system suggested faster routes, it was meant to work as an alternative for those commuters who could give up using their cars for traveling. Instead, we conducted a preliminary comparison against mainstream solutions, such as Google Maps, and the results are encouraging. In the future, we plan to benchmark the performance of our architecture against a myriad of traffic management solutions. Our architecture is unsurprisingly layered because it distinguishes and accommodates different types of transport modes. Layering the transport architecture enables us to represent its infrastructure using different types of software agents. In effect, each software agent type models segments that have similar physical characteristics. In our view, the layered architecture brings about multiple advantages, such as flexibility, modularity, and maintainability (e.g., separation of concerns), among other attributes. On the other hand, using a single type of agent (i.e., a non-layered architecture) to represent all transport segments is inefficient (i.e., with respect to computational resources and storage) and might introduce challenges if extensions or changes to the architecture are required. Even though the software agents contribute towards resolving common goals within the same system, they are naturally autonomous and independent entities capable of making their own decisions and can exist in remote environments independently. No single agent has a global view of the whole system, but rather it has a partial understanding of the conditions of the environment unlike centralized systems. Multiagent systems are also called self-organized systems as agents can exercise self-control and exhibit complex behaviors without any intervention from a monolithic entity. Implementing our intelligent multimodal traffic management architecture in a real environment, based on Figure 1, would result in cost implications. Firstly, our traffic management system would need to have traffic road sensors deployed in several parts of the transport network to feed the traffic data predictor so that reasonable traffic predictions are produced. The cost is highly reliant on (1) the number of sensors, which depends directly on the size of the city and number of roadway segments; (2) the type of sensors to