The canals of Amsterdam represent a draw for tourists and add to the value of real estate along the canals. More than 40 percent of Amsterdam tourists take a trip through the canals, accounting for more than 3 million canal travelers annually. Therefore, the tour boat sector is crucial for tourism in Amsterdam. Many of the quay walls on Amsterdam’s canals have either reached the end of their lifespan or need replacing because of damage caused by overloading and the overuse of heavy supply trucks. Currently, a length of 200 km out of the total length of 300 km of quay walls have reached the end of their 100-year service life, and some walls have even passed that limit. In 2017, five incidents of quay-wall failure occurred. In 2018, three such incidents occurred. Hence, these quay walls are in critical need of replacement. Many are located along busy canals, which means that closing these canals will almost certainly directly lead to the congestion of traffic flow on the water. This is highly undesirable for the tourist sector. At the moment, there is no research being conducted into how to manage quay-wall renovations without disturbing transport over water. The main goal of this research project was to develop a first version of a model through which the impact of the quay wall renovations on transport over water (e.g., passenger transport and pleasure craft) can be assessed. Thus, the main research question for this project was as follows: "How can the impact on nautical traffic flow and congestion patterns of quay wall upgrade works in the canals of the City of Amsterdam be assessed?” A model was constructed based on the Amsterdam canal traffic analysis. An agent-based modeling framework was selected as a suitable framework, because it could give insight into not only if and when congestion occurs, but also why congestion occurs. The ABM framework is able to represent the distinct sailing speed and route selection of each vessel type, can reproduce the (routing) behavior of the three vessel types that interact, and had the additional option to incorporate the irrational behavior. Each vessel was modeled as an entity with its own logic (step-by-step instructions) and making its own decisions while moving on a graph. In order to reproduce observed traffic flow and congestion patterns, modeled vessel objects needed to move through this network, calculate their routes based on the network structure and properties, and queue at crossings. Therefore, this study applied network logic (graph theory) and queueing theory. This project used the open-source NetworkX package in Python to construct a network (graph) based on the spatial coordinates of the Amsterdam canal network, with nodes at crossings and bridges and edges connecting the nodes. Each vessel object in the model used algorithms from the NetworkX package to calculate its route on the network. The model also used the SimPy package to simulate queueing and crossing congestion at crossings with discrete time steps. To summarize, the model simulated on a microscopic level, used discrete time steps, and applied the agent-based modeling framework. As this model is developed within a community setting at the TU Delft, this model, and other transport network analysis models, can be viewed and found at https://github.com/TUDelft- CITG/Transport- Network- Analysis