Using K-Shortest Paths with a certain level of overlapping (Semi-Disjoint Shortest Paths, hereafter, Dj-kSP) allows the use of routing alternatives more decoupled from the progressive congestion of the paths. This paper's initial hypothesis is expanded to cover the TWM generation based on Semi-Disjoint Shortest Paths and the optimal TWM assignment depending on the drivers' adherence to the routing method. Using Dj-kSP-based TWM should generate a greater route variety for the drivers, resulting in lower total travel time. Results should improve the direct k-Shortest Path and the TWM(k-SP) route assignment and approach the theoretical system optimum estimation. Additionally, optimal TWM assignment should enhance the performance of the optimal TWM generation strategy [15] as the complexity of the problem is considerably reduced. Considering that an \(M\) map set has been designed to route the different \(F\) traffic flows for \(V\) vehicles that use the traffic network, it is necessary to find the optimal map assignment for the drivers, a problem of \(O(V*M)\) complexity. This complexity may be reduced to \(O(F*M)\) when considering traffic flows instead of individuals. The TWM maps used for flow routing can be efficiently achieved using pseudo-optimal routes based on semi-disjoint shortest paths (paths with limited overlap) obtained by the ESX-C algorithm described in [16; 17]. Figure 1 provides a schematic overview of the Dj-kSP-based TWM map generation and distribution for traffic assignment. In this sense, this paper addresses:  
* A new method to create TWM map sets based on traffic flows, using the semi-disjoint k-shortest paths (Dj-kSP) linking their origin/destination nodes [13; 18]. These semi-disjoint k-shortest paths define the best low-overlapping routes for each flow and are used to create routing zones that promote traffic usage through them. * A study for optimal TWM assignment to the vehicles using a per-flow strategy. Optimization is achieved by applying genetic evolutionary algorithms (GA), to find which flow amounts will receive each TWM map. * An empirical study for real urban networks with synthetic traffic demands, comparing the results with basic traffic assignment policies for lower and higher bounds such as free-flow and all-or-nothing routing methods [5], and with system optimum estimation methods such as Successive Averages Method (MSA), Cumulative Assignment Method (CAM) [5] or the linear programming method proposed by [PERSON] [19]. * A discussion about the obtained results and the computing complexity of the different approaches. Calculating TWM maps based on routing areas around Dj-kSP seems to be a cost-effective solution for the problem. An initial optimal TWM distribution based on the Dj-kSP routes provides a good enough solution like a full TWM distribution optimization, thus offering a practical heuristic solution to the problem. Transit traffic is a main urban traffic demand component [4; 20], refers to the movement of people within the metropolitan areas, using public multi-modal transport such as buses,  
Figure 1: Schematic of the generation and distribution of TWM maps based on Dj-kSP (source: authors). trans, light rail, subways, and commuter trains. Urban traffic planning needs to address the challenges of congestion, environmental concerns, and the efficient movement of many people, dealing with route design, schedule and frequency of service, fare structures, integration with other modes of transportation, accessibility, safety, and user experience [21; 22]. TWM may be efficiently used for transit management with specific policies. Though it is out of the scope of this work, it constitutes a challenge for future research. The next Section 2 reviews the state-of-the-art and previous works. Section 3 describes the TWM and path flow model, and the optimization strategies are discussed in Section 4. Section 5 describes the materials and methods used in the research.