## 4 Materials and Method
### Traffic Model  
All traffic models comprise two main parts, which are the traffic supply and the traffic demand. Traffic supply determines the capacity of the infrastructures, whereas traffic demand describes the behavior of travelers. Traffic modelling simulates how these two components interact with each other. In the current study, the Open Source \"Simulation of Urban Mobility\" (SUMO) [35] software is used for developing the traffic model. The analyzed network consists of 7025 links and 879 nodes. Links have multiple attributes including, but not limited to, the free-flow speed, number of lanes, and capacity. The geometric configuration of the model includes 917 traffic assignment zones. The static origin/destination (OD) matrix included 462,720 vehicles in a day. Based on the initial static OD matrix and using the od2 trips tool of SUMO, a time-dependent OD matrix was estimated. The od2 trips is a program that generates trip tables from the OD matrix and assumes that the matrix is coded as the number of vehicles that travel from one district or traffic assignment zone (TAZ) to another in a certain time. In traffic demand modelling, a trip is defined with starting point (origin), end point (destination) and beginning time. The traffic demand data of the pilot zone was obtained from Infraestruturas de Portugal (IP), the Portuguese transportation infrastructures management. A constant demand was assumed, i.e., users do not change their travel mode because of the flooding. Figure 4: (**a**) Overlapping of the roadway network with water-depth map for a 20-year return period flood; (**b**) Instantaneous water level measurements at each hydrometric station (in meters). Od2 trips allows splitting OD matrices, which define a long time period, into smaller parts that contain definite percentages of the whole. It is possible to split the matrix into 24 subparts--this means the number of fields is fixed to 24--allowing to spread an OD matrix over a day describing it by hours. In dependence to the week day and the type of traffic, one could set the appropriate time line. Some common daily time lines from Germany are retrieved and could be found in SUMO documentation [20]. In the current paper since there is no available information regarding daily time lines from Portugal, data from Germany were used to determine the vehicles distribution over time during the simulation to approximate rush-hour flows. Figure 5 depicts the number of vehicles introduced to the network over time during the simulation for a 462,720-vehicle scenario. Because of the interactions of the individual drivers during their travel through the network, the route choice and travel times are strongly coupled. The route choice is based on travel times and the travel times result from the route choice. Therefore, it is necessary to apply a model for link flows that reproduce the dynamic findings of traffic flow. In this paper, a mesoscopic simulation-based dynamic traffic assignment (DTA) model on the road network of Santarem region was performed. Traffic assignment models, especially simulation-based dynamic traffic assignment models, are commonly used in a wide range of applications, including traffic management and transportation planning [36; 37; 38]. Route choice and network loading, as the main components of DTA models, aim to represent the dynamics of vehicle movements in a network. The Santarem road network was imported to SUMO using shapefiles of this network, which were received from IP. The evolution of the traffic flow over the links of the network once the route choice was determined are represented by network loading. Although the stability and the convergence of the simulation-based assignment algorithms usually cannot be proven, a realistic description of traffic dynamics which is essential with respect to the usability in real-world applications, could be gained. Generally, Santarem has a robust road transportation system, but there is a factor that may affect mobility in this network.