## 3 A Multimodal Mobility Recommender System for Urban Areas  
We start by giving an overview of the complete architecture that we devised to realize our intelligent transportation system, which integrated three vital software modules. Figure 1 shows the software modules and the fundamental interactions between these modules. The specific modular software tools are (1) the multimodal decision support tool, (2) the traffic congestion data estimator, and (3) the real-time traffic prediction tool. In essence, the multimodal traffic recommender suggests eco-friendly routes after receiving traffic flow forecasts from the real-time traffic prediction tool, an ant-based system for modeling and simulating traffic to predict stream characteristics [55]. This belongs to the class of holonic-based solutions [56]. The Erlang programing language was used to implement the real-time traffic prediction tool as it enables the creation of large-scale real-time systems that run a high number of parallel processes. The forecasts calculated here represent the costs of using transport segments that could take the form of estimated travel time or CO\({}_{2}\) emissions. Finally, the traffic congestion data simulator fetches road traffic information from the urban traffic management and control center (i.e., UTMC) every four seconds. Moreover, traffic data can be collected using floating cars (through dedicated in-car WIFI devices) and roadside sensors periodically. The servers of public transport provide live information about their services. The central role of the traffic congestion data simulator is to estimate the level of traffic and amount of CO\({}_{2}\) emissions and push these traffic estimations to the other two modules once every 4 s. Data are exchanged between the software modules using XML RPC and SOAP communication protocols. However, in this paper, we focus mainly on describing the multimodal mobility recommender system as it represents the core contribution of our work. Mobility demands are continually rising, especially in urban areas, posing serious traffic management concerns [57]. We opted for the multi-agent architecture as the implementation paradigm as it can help mitigate several challenges, including scalability, efficient use of resources, and coordination [12]. The use of a multi-agent computing paradigm for traffic transport management and optimization offers a myriad of opportunities for improving traffic flows, reducing transport emissions, and satisfying commuter demands [58]. The eco-friendly multimodal system we propose models and combines several layers of the urban transport network. Each transport segment is represented by a software agent capable of making autonomous decisions for it to be included or excluded in the routing calculations depending on the cost contribution of the transport segment (i.e.,carbon emissions or estimated travel time). Below, we outline a commuting scenario, which helped us derive the relevant requirements and observations for the proposed system. A motivating scenario: \"A commuter is regularly commuting to work from Marple to Piccadilly train station in Manchester, United Kingdom by car. On the day of the scenario, however, there has been an accident on A57 (part of the major route to the station), and one lane is blocked in the middle of a road segment. In such circumstances, the commuter uses a mobile phone application to search for an alternative route; the mobile phone app can serve as a frontend of the eco-friendly multimodal ITS system. In the app, the stored preferences, based on the patterns of past use, indicate that taking the tram directly from Marple is another viable alternative. Based on such recommendations from the app, the commuter chooses to take the tram today as this reduces his overall travel time and contributions toward carbon emissions\".