## 2 Related Work
Synthetic route cost modification by the ITS needs to be balanced with the driver's experience who takes the routing decision considering factors such as available network status information, previous experiences, or subjective concerns, as pointed out in [34; 42]. Traffic Weighted Multi-maps (TWM) consider that drivers with different usage needs and concerns (utility functions), may receive different network maps. It takes into account that a map is just a view of the (urban) traffic network composed of (a) a physical layer for the topology of paths, junctions, and crossings, (b) a logical layer with management conventions and rules (sense, speed, constraints, prohibitions, and others. ), and (c) a status/usage layer [34] showing network occupancy. An ITS generates a TWM comprising a set of map views for the same area (same physical layer) and a modified logical view formed by a distribution of edge/link weights based on the original network map ones. The so-formed multi-map (TWM) is distributed to traffic groups to create differentiated routing. Other related experiences have also pointed to map transformation procedures to generate alternative routes [43]. This technique may be applied to multiple use cases such as multi-objective travel optimization, congestion reduction, incident management, per fleet differential routing, and others [8]. Beyond these use cases, congestion management is a fundamental concern to transportation mobility linked to traffic equilibrium situations. TWM is a feasible technique compatible with both approaches as it combines different perspectives: (a) traffic network topology; (b) trip data (past and predictions); (c) usage costs; and (d) vehicle/trip segmentation based on activity models. TWM can be easily integrated into traffic planning systems based on the Trip-based Demand Model (TBM) or the newest Activity-Based Models (ABM) defined by the USA Transportation Research Board [44; 45; 46]. TBM's four-step model considers the following activities: (1) trip generation, (2) trip distribution, (3) transport mode choice, and (4) trip assignment. TWM mechanism may be considered between activities (3) and (4), just before the trip assignment stage, as the TWM routing maps should be distributed before it. Our previous works [8] explored how to create map sets that minimize traffic congestion in a network and how far are the solutions found from the theoretical user equilibrium and system optimum as defined by [PERSON] [23]. Dynamic traffic assignment (DTA) based on TWM was studied in [34], focusing on the study of the TWM adherence of driving collectives, considering their perceived utility evolution: when a driver uses a route based on the TWM map and receives a significant improvement in their cost function, their adherence to TWM will increase. The opposite effect will also occur if the utility decreases. User behavior and utility functions were modeled with fuzzy rule sets [47], concluding that the system will quickly evolve to a user equilibrium. TWM generation may also be addressed by applying existing alternative route computing algorithms to use the resulting paths to modify the TWM map weights, thus conditioning the vehicles to select alternative routes based on these complimentary network views. Assuming that traffic flows may be predicted based on previous data, TWM maps can be created based on the alternative route calculation. The TWM problem then focuses on the best map assignment and distribution aspects. The main objective for alternative routing is to reduce traffic congestion due to path cost increases caused by link occupancy. Path disjoint level is a critical factor in lowering link occupancy. If it is not considered, k-shortest paths obtained employing standard routing algorithms (such as Dijkstra) will differ in very few links, and so congestion will remain structural [13; 14]. [PERSON] et al.