## 1 Introduction  
The exponential increase in traffic volume has brought many potential challenges to the traffic engineers and transportation department in the form of a massive traffic jam [1]. Traffic congestion has become a severe issue for transport and logistics departments all over the world [2]. It causes a wide range of social, economic, and environmental problems. First, drivers face a higher chance of late arrival at their destination because of massive traffic jams, which creates a high level of anxiety. The driver stress can also turn to impatience, carelessness, and thus increase the possibility of traffic accidents and other social issues such as road rage. Secondly, there is also an enormous economic loss from traffic congestion [3]. Regardless of the extraordinary investment on transportation systems such as car navigation systems (e.g., Garmin, Waze, and TomTom), web services for route computation (e.g., Google Maps and Microsoft), and dynamic traffic assignment (DTA) [4], still ITS have persistent challenges (i.e., a time waste in traffic jams). Many solutions exist in the literature try to cope with the traffic congestion problems from different perspectives [5; 6; 7; 8]. The traffic surveillance system deals with the installation of cameras and loop detectors on highways [9]. It consists of a web-based service to monitor the current status of traffic to guide drivers. Although this system captures accurate information, it still has limitations, i.e., (1) the system is limited only to highways (city roads are quite difficult to control); (2) no real-time congestion detection service is possible. Google Maps [10], Waze [11], and TomTom [12] have gained increasing attention in recent years. However, Google Maps, Waze, TomTom are far to achieve the ITS goals, since these applications depend on information posted by people using their smartphones [13]. The traffic navigation applications collect mobile GPS data from the drivers and use infrastructure-based traffic information to compute traffic-aware shortest routes [14]. The solutions presented by Google Maps, Waze, and TomTom are reactive by nature. Mostly, the congestion is switched from one path to another because Google, Waze, and TomTom controllers provide the same guidance to all vehicles at the same time [4]. Moreover, routes suggested to users are based exclusively on shortest-path algorithms, without considering the impact of rerouting in future traffic conditions. Additionally, to detect the congestion on intersection point, different methods can be applied [15; 16; 17]. These include calculating the number of vehicles queuing, estimating road segment occupancy, and sensing the safety time headway [15]. Once traffic congestion occurs at intersection point, the vehicles begin to find an alternate route to their destination aiming to reduce travel time. Each vehicle behaves selfishly and attempts to move a non-congested route with a high preference. The authors in [16] used a non-cooperative game theory to solve the problem of optimizing the traffic jams on intersection. In the given game, every vehicle is considered to be a selfish player, where Nash equilibrium set traffic for each route in such a way so that no individual can improve its performance by changing its route [17]. [PERSON], [PERSON], et al. [4] proposed five (i.e., Dynamic Shortest Path (DSP), the \(A^{*}\) shortest path with repulsion (\(AR^{*}\)), the random \(k\) shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP)) cost-effective and easily to deploy traffic rerouting schemes for vehicular traffic management that reduce travel time. Their proposed schemes detect congestion proactively on each road segment by periodically assessing each section with a predefined threshold value (i.e., \(K_{i}/K_{jam}>\delta\)), where \(K_{i}\) and \(K_{jam}\) represent the current and the maximum number of vehicles, respectively.