of each driver at each moment modifies the working conditions of the network. It forces the rest of the drivers to react to the changes and adapt their routing decisions. The system evolves until an equilibrium condition is met. For this reason, the resolution of the TAP has usually been approximated by heuristic methods, such as traffic assignment methods. Traffic assignment methods abstract TAP as a supply and demand problem as pointed out in [5]. Vehicle trips (demand) are assigned to the traffic network (supply) by strategically performing route choices. There are multiple traffic assignment methods to model trip mapping into the network, either static or dynamic (DTA), and using direct or stochastic estimations [6]. They usually consume expensive computation resources as optimal paths must be calculated multiple times in an iterative way as the traffic network becomes loaded. [PERSON] and [PERSON] [7] compiled an extensive comparative study of the equilibrium and assignment methods. Traffic assignment methods provide a theoretical framework for studying practical ITS implementations or simulations to see their effectiveness when comparing their behavior with equilibrium situations. Classical traffic assignment and routing works consider that all the drivers share the same network view (map), assuming this is a fundamental hypothesis. Thus, algorithms rely on it and focus on other topics, such as edge occupancy and toll costs. The Traffic Weighted Multi-Maps (TWM) strategy was presented in [8] as an innovative mechanism of generating and distributing a set of complementary views of the traffic network to the drivers to generate path diversity chosen directly by the drivers. Moreover, TWM can be generated ad hoc for special traffic network conditions such as accidents or road cuts. TWM differs from other routing and traffic planning and assignment methods, providing network views as maps implementing user utility functions, system optimum constraints, dynamic reactions events, or any other type of information. TWM could also be interpreted as upgrading standard traffic signaling methods (fixed or dynamic panels), including a broader range of use cases. It is compatible with any other practical ITS implementations and can be directly integrated with them as it focuses on the map layers they all use. Finding the right TWM configuration is a key challenge, and the first approaches were focused on how to obtain optimal TWM link weights (costs) based on driving needs, regulations, network constraints, and congestion conditions. Optimization techniques have been explored, such as genetic (GA) or particle swarm algorithms [9; 10]. They provide good results for small or mid-size traffic networks. However, finding an optimal TWM for a graph containing \(E\) edge weights for a set of \(M\) maps is an \(O(E*M)\) complexity resolution problem. GA applied to TWM becomes computationally unaffordable for more extensive networks (see [PERSON] et al. [11] for city references). Beyond the optimal TWM generation studies, this paper explores a different heuristic approach for the TAP using TWM. The initial hypothesis considers that the main traffic flows in the network can be predicted based on historical data, so it could be possible to generate also predefined TWM based on the k-Shortest Paths for these flows and obtain the optimal TWM assignment for the vehicles toward a system optimum. Each driver can make the best routing decision considering the map view received and the road occupation status. TWM generation based on k-Shortest Paths (TWM(kSP)) and optimal path-flow distribution was presented as a preliminary concept in [12]. In most real networks, the k-Shortest Paths (kSP) between two nodes have subtle differences of one or two edges with minimal impact on the cost of the final path. As edges become congested, vehicles will select the following lowest-cost links. However, most of the links will remain in common. In some way, this route selection strategy induces congestion, as it does not promote route variety [13; 14].