## 8 Conclusions  
Though TWM has yet to be deployed in real traffic scenarios, simulations show that using Traffic Weighted Multi-maps effectively creates alternative routes for traffic planning and assignment. The complexity of new ITS deployments requires extensive previous simulation. TWM management involves four main stages: map generation, map allocation, map distribution, and map adoption from a dynamic econometric utility perspective. They are compatible with the existing traffic planning models (TBM, ABM). Our work focuses on the first two stages. On the TWM generation side, previous works focused on optimal TWM creation and dynamic usage models. Optimal TWM generation requires intense computing effort because of the complexity related to the network's size and topology, and utility models. Our new approach shows how to create pseudo-optimal TWM distributions based on the traffic flows, creating disjoint path-flows that determine routing areas, and allowing a wide variety of parametric configurations and policies. The partially disjoint paths are created using the ESX-C algorithm defined in [16]. However, instead of using the returned disjoint paths for direct vehicle routing, they are used to generate routing areas in the traffic maps. The routing agents autonomously decide their final routing, considering the maps received and traffic status (links occupancy). The TWM(Dj-kSP) map set could be distributed with an optimal assignment. However, it should be optional from a practical perspective, as optimal criteria depend highly on the drivers' adherence to the TWM proposed maps and the disjoint features used to generate the maps. The disjoint routing areas perspective creates the necessary path variety with partially overlapping paths. The experimental environment has been developed by simulation on a real urban network using synthetic traffic flows that can be easily implemented in any urban environment. The challenge here is to identify the necessary traffic flows and demands, which is a well-known problem that traffic operators already manage. Previous works on TWM [8; 15; 34] have showed that it can coexist with existing ITS helping to mitigate congestion effectively. It should be implemented as a complementary system, as in the real traffic world, multiple ITS systems coexist (centralized, distributed, per vehicle vendor, per ITS solutions vendor, etc.). TWM can be easily integrated with any existing ITS as it implements a map service. The TWM method stands out from an innovative standpoint in the following ways:  
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* Driver decision capabilities are preserved to select the best route choice. * Utilizes conventional optimization algorithms and techniques for route calculation. * Leverages existing data sources like Smart-Cities and OpenData, while adding further value. * Seamlessly integrates into present traffic control systems, introducing a novel routing module that utilizes distinct maps as defined by TWM. This incorporation does not necessitate extra infrastructure installation. * Ensures compatibility with existing traffic agents from the user's viewpoint, replacing the maps they currently employ. * Requires no universal adoption by all vehicles; it can be selectively employed, potentially biasing its usage for specific categories or policies. * Operates without reliance on V2V communications or the deployment of sensors, panels, or communication networks. Among the main limitations of TWM are the following:  
* Requires the figure of a network operator operating the ITS. * It is a heuristic method as it induces driver and fleet behavior, and its results may be affected by the ITS that uses it. * Under low traffic demands, its results have a low impact. An ethical discussion may be addressed by serving different traffic network views to different drivers depending on their differentiated utility requirements and also from the perspective of the traffic system operator to achieve a good network performance.