Against the backdrop of the increasing maturity of connected automatic driving technologies and the gradually expanding market share of CAVs, this thesis explores the optimal traffic management strategies to cope with road closures in the context of Connected and Automated Vehicles (CAVs) and Intelligent Transportation Systems (ITS).
A rerouting strategy is designed based on the rerouting behaviour of vehicles when road closure occurs in life, the control parameters include the control of the CAV's automatic rerouting period, rerouting probability, HDV Knowledge of the time of lane closure, as well as their rerouting probability. The aim of this study is to find the optimal combination of these five parameters. Four levels of CAV penetration (20\%, 40\%, 60\%, and 80\%) are considered with the objective of minimizing the total travel time on a mixed CAV and human-driven vehicle (HDV) traffic flow network. The main question is \textbf{What is the optimal rerouting strategy for CAV and HDV mixed traffic when road closure happens?} and in the process of answering this question, the effects of CAV penetration, individual rerouting parameters and different road closure locations are considered and analyzed.
In this thesis, a simulation-based approach is used to model the traffic flow applying both micro and meso scale models. Then, the simulation is conducted for the predefined scenarios, then the sensitivity analysis of each relevant parameter is performed using a one-factor-at-a-time approach to understand the impact of each parameter on the network traffic condition. Finally, Bayesian optimisation is used to find the optimal rerouting strategy within a certain search range and number of times, where the results obtained from the sensitivity analysis are used to determine the parameter search space.
The grid network and the Sioux Falls network are simulated respectively and the relatively optimal rerouting strategies are found for them. The grid network can be regarded as a local area on the network, while the results of Sioux Falls, as a larger network, can provide some basis for city-level traffic management.
The key findings of this thesis include (1) CAV penetration increases bring reductions in TTT and TWT and increase in TTD to the network, overall, the traffic flow movement improves and severe congestion decreases, and network conditions improve significantly during the growth phases of 20\%-40\% and 60\%-80\%; (2) the importance of each rerouting parameter varies for different networks and at different penetration rates, and the results fluctuate significantly between different test values, with no single increasing and decreasing trend; (3) road closures at entrances and exits located at intersections are more critical and require targeted rerouting strategies; the traffic demand distribution has a significant impact on it; (4) Bayesian optimization can find the optimal rerouting strategy in a finite amount of time, where the specific strategy and the improvement effect varies for different networks and levels of demand.

Amsterdam's extensive network of bridges and quay walls is facing a critical maintenance backlog due to structural underfunded infrastructure maintenance. The many upcoming bridge maintenance projects to ensure safety are expected to cause significant more hindrance to road users. However, complex road networks with high traffic density in urban areas like Amsterdam make it difficult to foresee how the effects of different maintenance projects interact and how the projects can be planned efficiently to reduce hindrance.
This study aims to optimise the planning of a given set of bridge maintenance projects to reduce hindrance to road users, combining a genetic algorithm with traffic simulations using a new estimation method to improve the assessment of simultaneous closures. The genetic algorithm selects the optimal starting time and execution duration for each bridge while minimising maintenance cost and additional travel times for the modalities car, freight and bicycle. Using the fast static traffic simulation of the Urban Strategy Tool of TNO, a new estimation method for evaluating simultaneous closures is proposed, accounting for interdependencies between bridges. Results of a case study on four urban bridges in Amsterdam show that the new method provides more accurate estimates of additional travel time compared to previous methods and generates better solutions. Moreover, the fast proposed optimisation framework makes it possible to evaluate multiple scenarios in reasonable time to assist in decision making of maintenance planning.

The outbreak of the COVID-19 pandemic and the resulting corona crisis has had a profound impact on mobility in the Netherlands and in the entire world. This study aims at predicting, modeling, and analyzing changes in the activity-travel behavior of individuals in the Rotterdam-The Hague Metropolitan Area, the Netherlands, during different stages of the COVID-19 pandemic and assess their effects on mobility. This is done by using the activity-based travel modeling approach. A modeling framework is developed and used for this study. The modeling framework provides an innovative approach to study the impacts of changes in activity-travel behavior caused by emergency situations such as the COVID-19 pandemic in a disaggregated manner. It combines the outputs of ABMs and a mix of aggregated and disaggregated data of changes in in-home and out-home activity frequencies and re-estimates the daily schedule of individuals considering factors and attributes that were not considered before, such as the weekly frequency at which agents do different activities and the sector where agents work. With the outcomes of the case study simulations, insights about the effects on mobility were identified and compared to real data counts and results of other studies. The conclusion is that the vast majority of the model outcomes proved to be in line with the results of other sources, and the modeling framework can be a useful tool to study the changes in activity-travel behavior of individuals during emergency situations and their effects on mobility.

In recent years, the amount of traffic on the highways has increased continuously and in particular freight traffic. Despite solutions to maximize roadway capacity, the drop of capacity after congestions sets in, remains an active field of study. The influence of freight traffic on traffic flow has received little attention, especially regarding the capacity drop. The Kaplan-Meier Product Limit Method was used to estimate capacity and recovery distributions for selected sites to overcome stochastic characteristics of traffic flow and investigate the relation between heavy vehicle share and capacity drop at Dutch highways. Furthermore, a simulation study was executed to investigate increased heavy vehicle share scenarios and changing physical and operational characteristics of heavy vehicles. The empirical results show a connection between heavy vehicle share and the capacity drop, although not statistically significant. Currently, chaotic properties of breakdown flow seem to superimpose the impact. However, it is possible that the effect becomes influential as the current maximum observed share of heavy vehicles during breakdown grows from 9% up to 15%, which appeared to be the worst case scenario in simulation. Besides, concern is raised as a decreasing breakdown capacity was observed on several Dutch highways, even after correction with the PCE values of the increasing heavy vehicle share.

This research focuses on the analysis of Floating Car Data (FCD) data to understand sustainable transportation behavior in the area of Amsterdam. Using data collected by mobile devices, trips are analyzed by their distance and transportation method. Sustainability is a relevant factor to the social and scientific community. With increasing population and growing cities, the impact of travel on the environment is also increasing. New policies are required to stimulate a more sustainable approach to transportation. Research on sustainable travel behavior provides input for the policy makers. The main research question is defined by what extent FCD can be used to provide insights in the sustainable mobility behavior in Amsterdam. In the existing literature, there are four objectives for sustainable mobility: hazards reduction, travel reduction, modal shift and accessibility. The modal shift (e.g. replacing care usage by public transport) is one of the key drivers for this research. To quantify the behavior, different sustainable mobility indicators are identified to determine the sustainable direction related to the mode of transportation. In the literature there are many methods to quantify sustainable transportation behavior ranging from the traditional methods like counting and surveys to modern approaches including smartphone and sensor data. The methodology used starts with the problem statement and literature review. The data sets are selected and analyzed, providing the results and conclusions. A decision tree is used for categorizing different trips people make, where a difference between short and longer distance trips is made. This research makes use of a wide range of tools ranging from PostgreSQL databases to advanced features of ESRI to visualize data. Five different available data sets are analyzed for their suitability for this research. Based on several requirements like availability, having Origin and Destination (OD) information, usability and documentation, the data sets are assessed and two data sets (Google OD and LMS) are chosen to be analyzed in more detail. The data is filtered and cleaned to make sure it fits the scope (Amsterdam) and the two sets are compared to each other. The trips are split into short and long distance trips, where for both a detailed analysis is performed on the trips which can be easily replaced by more sustainable trips. Regarding the short distance trips, analysis shows that walking and biking are the most common option in busy areas as the city center and the business district. The less sustainable car trips for short distances show several patterns in the city. Using interactive maps, these patterns are identified and both data sets are compared. For longer distance car trips, train transportation is the more sustainable option. There is however a tradeoff between the most sustainable and least time-consuming transportation option. This makes that train trips are not always the most logical or even sustainable choice for transportation. A method is developed and applied to the data set to test if a train trip is a realistic alternative. The results show for both data sets that there is a significant amount of car trips which could easily be replaced by the train. During the analysis of the data for long distances, a problem has been found with the Google data set. It seems that this data set is showing most of the time the same noise. This phenomenon was found by comparing the different modes of transportation available in the Google data set. The error is not in all the data, but it seems to have an impact on the data set. Conclusion is that the data provides many insights on travel behavior and different available data sets can be linked together to provide deeper insights. The Google data set shows interesting results for shorter distances but gives less reliable results for longer distances. The LMS data set is used to compare the results for both short and long distances. The use of FCD data to study and stimulate sustainable mobility behavior seems very promising, but the quality of the available data sets has a large influence on the usability.