## 2 State-of-the-Art Intelligent Transport Management Systems  
Indeed, the literature offers several intelligent approaches to model, optimize, and resolve traffic problems [19]. Such approaches comprise analytical and mathematical models. The analytical approaches focus on analyzing the traffic network equilibrium before any planned disruptions to the transport network [10; 20; 21]. However, the mathematical approaches, such as the graph theory, assist in modeling the resources of a transport network. The nodes of the graph represent road intersections and graph links represent the urban road segments and their carriageways [10; 22]. Petri net is an example of a mathematical modeling language that can be used to describe events within decentralized systems, such as the transport network, where transitions and flow of traffic are shown [23; 24]. Queuing simulation models help to simulate and analyze traffic behavior and flow in different sections of the transport network, e.g., red lights and stop signs, where the available capacity fails to satisfy the required demand [25; 26]. Recently, prominent works introduced intelligent transportation systems (ITSs) to empower the respective stakeholders to simulate and manage the transport infrastructure and its moving objects [27; 28; 29]. Some ITS implementations employ agent-based computing to offer distributed applications that react to the highly dynamic nature of traffic. In agent-based solutions, software agents are used as the building blocks to represent the traffic characteristics. The agents are self-organized, self-contained, and autonomous software entities that act on behalf of other users (e.g., cars, road segments, traffic lights, and so on) to accomplish common goals. In contrast to centralized problem-solving, intelligent agents of a multi-agent architecture communicate, interact, and collaborate to solve complex problems in a distributed manner [12; 30]. Naturally, traffic is highly dynamic and geographically distributed in the transport network, making it perfect for modeling using multi-agent computing [9; 31; 32]. Moreover, it is expected that a distributed computational solution, such as the multi-agent architecture, will outperform centralized modeling systems owing to its autonomy and flexibility [32]. Multi-agent computing was applied to overcome several transport challenges, including urban traffic control [33; 34; 35; 36], fleet management [37; 38], and route planning and guidance [39; 40]. Different agent-based frameworks were used in these applications to implement multi-agent environments, e.g., MATsim [41]. Artificial intelligence-based systems proposed for managing and optimizing the transport network have employed bird swarm optimizer, rule-based fuzzy logic, and artificial neural networks, among other techniques [39; 40; 42; 43]. In essence, these systems are trained on the previous traffic data to prognosticate the traffic stream variables in the transport network. Such predictions are used to manage the traffic flows and optimize the recommendations of the ITS. Recent traffic modeling systems addressed traffic management problems by integrating multiple modes of transport and considering eco-friendliness [44; 45; 46; 47]. For example, a multi-agent decision support framework used the genetic algorithm to optimize transport graphs and recommend the best combination of transport modes to commuters [45]. However, our route recommender architecture simulates the transport network at a macroscopic level, i.e., creating transport agents for every road segment and considering real-time traffic information for them, and thereby provides more accurate estimations of the traffic situation, aggregation of travel times, and travel carbon emissions. In addition, our system encourages sustainable transport modes, such as walking and cycling, which can expand on co-modality and multi-modality. In another example, a route recommendation system was developed to search for the best solution that suits user preferences while considering eco-friendliness [44].