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.

The Netherlands is like no other country enthusiastic about cycling and is the home of 37.000 kilometers of bicycle paths. These bicycle paths need to cross other modalities and therefore intersections are needed. At signalized intersections, the control is mostly regulated using sensors such as push buttons and one or multiple inductive loops. Sensors for cyclists are running slightly behind in comparison with those from cars and the company of CycleData anticipated on this fact by developing a new sensor, the iSignum, that works on radar and laser service and is capable to determine speed. The iSignum is capable of communicating with the iVRI (intelligent traffic light control) where it is installed and notify that a cyclist is coming. The sensor is placed further away from the stop line meaning there is time to communicate with the iVRI that a cyclist was detected and for example with what speed. This can benefit the comfort and traffic flow of cyclists when a notification leads to green light such that cyclists simply do not have to stop unnecessarily before the intersection. Part of this research was a literature study towards stakeholders and existing bicycle sensors. The latter was needed to be able to make a comparison of what makes the iSignum stand out from the rest or if it is just another bicycle sensor. A literature study has indicated that inductive loop sensors in combination with push buttons are used the most. Additionally, there are other cyclists sensors on the market nowadays. These sensors include infrared sensors, smart cameras and mobile phones and WiFi/Bluetooth applications. These are however not used on a big scale yet. In this report, research was done towards the newly developed sensor that is installed at an intersection in Delft, the Julianalaan-Nassaulaan intersection. Several matters were looked into, such as the accuracy of the iSignum in comparison to the reality at this location and what potential situations can result in disturbances in detecting cyclists. Furthermore, preventions are explored to an extent of what could be possible at this specific location. To test the exactness of the iSignum, manual bike counting is done over the course of three days. This data was then compared to the data that the sensor of iSignum logs. Conclusions that have been made after the comparison is that the newly developed sensor is not as accurate as the company CycleData claims it to be, the promised 95% is not reached. During the measurements, the sensor did not manage to pass the 78.2% accuracy. However, disturbances that happened, such as the presence of a delivery van or garbage truck or cars that drive over the bicycle path, while counting cyclists are included. Making the preciseness less in particular cases, with the worst case being 75.3%. V-log data, which logs traffic status and information such as detection and signal groups from an iVRI, is additionally used to check whether those disturbances can be covered by the existing control of the intersection. The outcome states that the existing intersection control of a loop sensor and push-button manage to still detect cyclists during the time of disturbances. Next to this, prevention methods for the disturbances are researched such that relying on the existing intersection control may not be needed in the future and the full potential of the iSignum can be reached after more precision from the sensor itself is achieved. A prevention method that could help all the occurred disturbances is to move the sensor more downstream from the bicycle path where the disturbances do not influence the sensor anymore. This is therefore also the recommendation for this particular intersection such that unnecessarily errors are filtered out of the data. Additionally, further research is needed after the relocation of the sensor and towards the enhancement of the accuracy because other errors are present that are not accountable to a disturbance. It is valuable to do more extensive research on this topic to determine if these errors can be erased from the system to reach the full potential of the iSignum such that the objective of shorter waiting times and more comfort for cyclists can be reached at signalized intersections.

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.