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.