Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation mod-els, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assign-ment are used to build the base case model. This base case model is calibrated using the traffic count data. Also, the validation of the model is performed by comparing the real (ex-tracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accu-racy. The model will be helpful for road authorities, planners, and decision-makers to test dif-ferent scenarios, such as the impact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.