Automated vehicles are conventional vehicles equipped with advanced sensors, controller and actuators. They achieve intelligent information exchange with the environment through the onboard sensing and cooperative system. vehicles are possible to have situation awareness and automatically analyze the safety and dangerous state of journeys. Finally vehicles can reach destinations following drivers' willing. The ongoing research on intelligent vehicles is mainly about improving the safety, comfort, efficiency and provide an excellent human-car interface. As a self-organizing system, the traffic system is quite complicated. There are many disturbance factors to lead to various traffic problems. One of the daily occurring problems is congestion on the motorway. In order to reduce congestion, Rijkswaterstaat applies various dynamic traffic management (DTM) measures to guide the traffic. It works well nowadays in conventional traffic. However, automated vehicles entered the market recently and will start to play an essential role in future traffic. The automated vehicles' reaction to DTM measures may be different from conventional vehicles while the traffic problems still exist. Therefore, it is necessary to research the effectiveness of current Dutch traffic management in automated vehicles. This thesis aims to investigate the effectiveness of current Dutch DTM measures with driver assistant and partially automated vehicles. Due to the time limitation, only the ramp metering measure will be researched through a simulation study. Therefore the main research question is 'How partial automated driving influences the performance of current Dutch dynamic traffic management system and how can this be evaluated via simulation?'. Three methods are applied, including literature review, simulation and statistical analysis. The literature part reviews levels of automation, various longitudinal and lateral vehicle motion models, which are chosen and modified in the simulation. Many ramp metering algorithms are also introduced in the literature review. The ramp metering controller in the simulation follows RWS algorithm. Besides, the motorway demand and the penetration rate of level 1 and 2 vehicles are two input of the simulation. From the simulation results, it is concluded that the level 2 automation consisting of Adaptive Cruise Control (ACC) and Lane Change Assistance (LCA) system brings a negative impact on the motorway capacity. The ramp metering measure remains efficient if the penetration rate of level 2 vehicles is low. However, when the capacity reduces to the critical flow set up in the ramp metering controller, Ramp metering loses its efficiency. The parameters in the ramp metering controller therefore, require an update. For further research, it is recommended to simulate the same scenarios with different ramp metering algorithms. Since the functions of the algorithms are different, there might be other robust control algorithms for automated vehicles. Besides, another limitation of this thesis is that the automation system in level 2 vehicles is defined as Adaptive Cruise Control (ACC) plus Lane Change Assistance (LCA) system. Other partial automation systems may have a different effect on the performance of ramp metering. This thesis can be expanded by research the ramp metering performance under various types of partial automation systems.

Microsimulation can substantiate traffic safety analysis. The questions is how to use the information from microsimulation to say something about the general safety at network level. The objective of this research is to find out how microsimulation can be used to quantify traffic safety at a network level. For this, the surrogate safety assessment module was used to identify the conflicts that occur on a large scale network consisting of a distributor road with 20 intersections. These conflicts are indicated by the Time to collision (TTC) and the post encroachment time (PET). Five years of historical crash data of this same network was compared to the conflicts from the SSAM both visually and statistically. The statistical comparison used the spearman rank correlation to check the correlation for different threshold values of bot the TTC and the PET. An AM-peak and PM-peak simulation scenario were run and compared to the crashes within the same time interval during the day. The AM-peak conflicts showed a reasonable fit with the AM-peak crashes with a spearman coefficient of 0.58 and a corresponding P-value of 0.008 for TTC and PET values up to 1.0. Which is significant within a 95% confidence interval. The PM-peak conflicts showed a bad correlation to the crashes on the network with a spearman rank coefficient of 0.275 and a corresponding P-value of 0.240. To see if there was a connection with other surrogate safety methods, a safety performance function (SPF) from the federal highway administration was used to predict the crashes on the network using the traffic volume. The use of this SPF did not lead to reliable results. The last stage of this research was the application of a newly proposed framework that consists of a linear model. Within this linear model the number of conflicts for each of the conflict indicators of the SSAM was compared to the number of crashes for each intersection at the network. By solving the linear model with a least square estimate the influence of each individual conflict indicator was estimated for different number of intersections. From these estimations, it could be seen that the TTC has the best predictive value out of the conflict indicators present in the SSAM program.

Many municipalities try to encourage cycling in their cities to increase the use of sustainable modes of transport and decrease congestion. They therefore implement innovative traffic solutions such as `green wave'-apps for cyclists. These `green wave'-apps influence the green phase of traffic lights by requesting a green light earlier than conventional detectors would. These apps have already been implemented in several municipalities in the Netherlands and Germany, but not much research has been done into this kind of apps. The goal of this thesis was to determine how these apps can contribute to the cycling experience and benefit both cyclists and municipalities. This thesis involved a literature review, theoretical case studies into the potential effects of the apps, interviews with municipal employees and app developers, and a small survey among app users. This research showed that when the request of a green light can be granted immediately, the use of a `green wave'-app can decrease the travel time of an average cyclist with up to 4.4 seconds per intersection. When an intersection already has more than one detection loop for cyclists, the advantages of using a `green wave'-app are more noticeable for the faster cyclists. Interviews with municipalities showed that the few reactions they got from users of the apps were mainly positive, but for most cities, specific results of the apps are not yet known due to a low number of users and a lack of research. Only in Enschede and in Marburg (Germany), a decrease in average waiting times for cyclists seemed to be found in research. The respondents to the survey showed mixed experiences, which was in line with the results of a survey conducted in Enschede. Some respondents did not experience benefits from using the app and stopped using it. More frequent users more often seemed to experience an increase in the number of times they got an early green light while using the app. Suggestions from respondents for possible improvements of the apps mainly focused on the implementation at more intersections. Municipalities saw possible improvements in the feedback to the users in the app. More information about where and when a cyclist got an early green light while using the `green wave'-app could increase the transparency of the app and show users the benefits from using it. The results indicate that the existing `green wave'-apps for cyclists can contribute positively to the experience of cycling and already seem to decrease waiting times for cyclists in some cities. This more positive cycling experience could theoretically contribute to the popularity of cycling and help municipalities encouraging bike usage. However, experiences of cyclists are not all positive. The suggested improvements of the apps could help to improve user experiences and further increase the number of users. Further research could simulate the impact of the use of these apps on traffic flows at intersections or look at the importance of the route prediction compared to only GPS-tracking.

Based on incident characteristics and quality of loop data and floating car data, a new incident detection algorithm is designed based on floating car data. This new algorithm can detect incidents on lane level by comparing the number of lane changes for a situation without an incident with a situation with a possible incident. Floating car data can give information about the number of lane changes if the accurancy is high. The floating car data is used as input for the new algorithm. The results of this new algorithm are comparable or better than the current McMaster algorithm, depending on the available penetration rate of the floating car data.

Modern societies are heavily relied on efficient transportation systems for mobilizing people and goods. These systems are mainly constituted by road traffic networks. Currently, traffic demand is immense and perpetually increasing with unprecedented rates that traffic congestion has become an imminent subsequent. Over time, all this human activity that has established the status-quo of modern societies has been negatively influenced by climate change. Climate deviations are prominent in urban environments with a higher frequency and elongated time scales. Therefore, road traffic systems jeopardizing their robustness, and their resilience is at stake. A fundamental component in road traffic systems is the human factor. Nevertheless, human factors, to the contribution in traffic, are largely neglected. Some other times we consider that humans act rationally. Consequently, engineers seek answers to questions of how to incorporate the human factor into the system to explain the behaviour of human drivers under adverse weather conditions. In this contribution, an exploratory simulation study was used to put into perspective the derived conceptual frameworks and assess their performance in terms of efficiency and safety. Various psycho-cognitive mechanisms were utilized to address the human factor and rationally connected with the vehicle motion to reproduce the traffic phenomena that we observe under the conditions of rain and fog.