## 1 Introduction  
Traffic congestion remains a global challenge in the transport domain, causing significant greenhouse emissions [1]. Other repercussions of traffic jams include prolonged travel times, traffic accidents, air pollution, noise, and disturbance [2]. However, the emergence of the so-called \"smart cities\" emphasized the need to implement and offer smart transportation as a service to the relevant stakeholders [3]. Moreover, the proliferation of intelligent traffic management systems [2] has opened a new horizon for realizing energy-efficient and carbon-free transport. Such systems undergo three primary phases. They usually start by collecting traffic data from heterogeneous sources (e.g., road sensors and mobile phones) available within the transport network. Next, the sensed data are cleaned, processed, and stored in appropriate formats within dedicated databases. Finally, smart services, such as emergency management and route guidance, are composed, tailored, and delivered according to the requirement of the relevant entities (e.g., commuters or traffic management authorities). Intelligent traffic management systems serve different services, including congestion avoidance, accident detection, and route suggestion, among others. Although the prospect of fuel efficient vehicles (e.g., hybrid and electric vehicles) has recently enjoyed wider acceptability and adoption [4], and stricter CO2 emission regulations have been introduced [5], road transport still produces approximately a fifth (21%) of the total carbon footprint in European countries [6; 7]. This increasingly worrisome phenomenon calls for further efforts to develop intelligent mobility frameworks that assist in (1) promoting sustainable commuting behavior and (2) balancing the flow of traffic across the entire transport network to reduce traffic congestions. In such mobility frameworks, sensing and navigation technologies play a pivotal role in recommending less congested routes that capitalize on multiple modes of transport by incorporating public transport, cycling, and walking. These technologies heavily rely on a transport network model and their perception and understanding of the current traffic conditions. Intelligent transportation systems (aka ITSs) use various techniques to model the transport network [8; 9; 10]. Typically, to model and organize transport, one has to simulate (1) the static entities representing the physical infrastructure, e.g., road segments and intersections; and (2) the moving entities, e.g., vehicles. A traffic management system senses the traffic data and implements the routing algorithms to eliminate traffic jams, with the aim of producing faster and cost-effective journeys. The agent-based paradigm [11] is among the popular approaches used for modeling transport networks because of its advantageous characteristics, including autonomy and decentralization. These advantages enable dealing with unpredictability in urban areas, e.g., [12]. The literature provides ample examples that demonstrate the importance of intelligent transportation systems. For instance, the authors of [13] present Adapt-Traf, a hierarchical organizational multi-agent architecture, which models the real-time flow of traffic to overcome road traffic jams. This approach endeavors to provide unimodal route guidance to car-only commuters. In another example, Green Travelling Planner represents a multimodal travel planner that combines 11 modes of commuting using a heuristic approach and commuters' travel needs [14]. However, the proposed tool does not use the multi-agent architecture, restricting its scalability and flexibility. Moreover, both examples do not use CO2 emissions as a factor to calculate and provide route recommendations; instead, they rely on travel time estimations from historical datasets.