Autonomous Driving Systems (ADS) are an innovative solution to urban mobility problems and have the potential to transform the Dutch roads radically. However, before these systems can be widely adopted, they must undergo a rigorous certification process to meet safety and reliability standards. Regulatory authorities are currently grappling with the challenge of certifying ADS responsibly. Despite significant technological progress, there is still limited understanding of the root causes behind these certification challenges. Without a clear understanding of these obstacles, finding effective solutions remains difficult. Therefore, this research aims to explore the barriers to responsible ADS certification and provide insights into how these challenges can be overcome.

Platooning has become a useful area for better transportation efficiency on highway driving. As Cooperative and Automated Vehicles continue to evolve and integrate , it is important to have insights into their implications, emphasizing the need for rigorous real-world assessments. In general, platoon formation is monitored by Cooperative Adaptive Cruise Control (CACC), which uses real-time vehicle-to-vehicle (V2V) communication to exchange vehicle status information, improving the control reaction as platoon members adjust to their surroundings. Automated systems can normally drive vehicles to perform planned behaviors based on the pre-setting by humans, but if the platoon encounters disturbances, the extent to which the automated system can still follow human intentions is still unknown. This research uses field operational test (FOT) data from the CACC platoon on an arterial corridor to assess the platoon's performance when disrupted during the test. This research applies the concept of meaningful human control (MHC) with focus on tracking condition. Additionally, this study will focus on human 'reasons', both distal and proximal. An evaluation framework for platoons is created by categorizing 'Tracking' into three main metrics: comfort, safety, and local stability. Furthermore, this study demonstrates that disturbance has variable degrees of detrimental impact on the platoon's tracking state, and that these effects may be recovered when the disturbance has concluded; however, different disturbance situations indicate different recoveries. The evaluation methodology of this paper provides insight into the tracking performance of CAVs, which can help road authorities build infrastructure for their wider deployment of CAVs. Last but not least, this study may provide guidance to automation technology organizations and automobile manufacturers on how to develop vehicles so that they follow human reasons more closely.

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.

Approximately 200,000 individuals travel to & from Schiphol Airport daily through various means such as cars, taxis, buses, shuttles, trains, motorcycles, scooters, and bicycles. With this perspective, Schiphol Airport is the largest mobility hub in the whole Netherlands, making accessibility a critical aspect. The Schiphol Group is responsible for providing/ensuring accessibility. Their main goal is to alleviate accessibility for Schiphol's customers, including passengers, personnel/commuters, business partners, and cargo, by focusing on different transportation modalities.
To achieve the goal of “the best airport for accessibility and sustainable aviation as well as land-side transport in Europe,” Schiphol Group works toward a car-free, emission-free vision and plans to apply on-site.
This thesis conjugates Schiphol’s and European Union’s (EU) goals (EGD, TULIPS) set for the aviation sector and further investigates the possibility of reaching the “Car-free Schiphol Centrum”. The objectives designated the reach this primary goal.
- A combined method to design and evaluate the car-free Schiphol Airport efficiency
- A system that uses the methods to display the efficiency of the car-free Schiphol Airport.
By exploring these objectives, the thesis aims to contribute to the overall goal of making Schiphol Airport a sustainable, accessible, and car-free hub for all commuters and users. The problem statement and main research question gather around this unifying goal:
“How can Schiphol Airport become car-free in its land-side areas?”
To address this question, the thesis proposes a combined method to design and evaluate the efficiency of a car-free Schiphol Airport. This method considers the goals set by the Schiphol Group and the European Union.
The methodology constitutes the combination of the literature-based frameworks to create anew the thesis as well as finding the best measurement tools to seek the results of a successful car-free Schiphol Centrum. Car-free Development and Transit-oriented development allow for discussing and creating the framework for a car-free Schiphol Airport. The evaluation of this framework will be done by implementing new modalities to the Schiphol to ensure a possible/potential modal shift of the users and assess the walkability of the land-side areas.
Along with the theoretical work, a case study for the thesis is conducted to apply the proposed methodology and evaluate its effectiveness. The case study focuses on the land-side areas of Schiphol Airport and aims to assess the feasibility of implementing car-free measures in these areas.
The case study's findings show that a car-free Schiphol Centrum is feasible and should be implemented in real life. The proposed measures depict such as improving public transportation, promoting cycling and walking, and providing efficient and sustainable alternatives to private cars (micro mobility options), can contribute to reducing car dependency and creating a more sustainable and accessible airport.
Overall, this thesis provides valuable insights and recommendations for achieving a car-free Schiphol Airport. By combining theoretical frameworks, measurement tools, and a practical case study.

Congestion is a major problem on the Dutch road networks, which is a widely researched topic. Multiple factors contribute to this increasing congestion, examples of these are increasing traffic demands and inefficient traffic signal control. The focus of this research is on arterial traffic networks consisting of signalized intersections. The effects of two innovations that aim to reduce congestion have been evaluated, which are intelligent traffic signal control using the data of connected vehicles and truck/vehicle platooning. Research has shown promising results in both areas, but there are still some gaps to be filled. Especially in the area of truck/vehicle platooning on intersections and the combination of intelligent intersection control and truck/vehicle platooning. These two innovations have therefore been combined in this study to develop a dedicated traffic management system that leverages vehicle connectivity and platooning capabilities to control the traffic on a signalized intersection. The objective of this study is to determine the traffic flow effects that this system has in mixed traffic conditions. This leads to the following main research question:

What dedicated traffic management system can potentially improve the traffic flows on a logistic corridor where truck platoons drive in mixed traffic (consisting of regular and connected vehicles) and what are the traffic flow effects on this corridor when the system is implemented?

The effectiveness of this dedicated traffic management has been determined by doing a microsimulation on an existing logistic corridor. This study is commissioned by the Province of Noord-Holland, so the chosen corridor for this is located in Noord-Holland. This corridor is a part of the logistic corridor between the largest flower auction in the world, Royal FloraHolland, and the A4 highway. The used part of the corridor consists of three intersections, from which the intersection between the N201 and Koolhovenlaan has been used to implement the dedicated traffic management system. The currently used signal controller on this intersection is a vehicle-actuated signal controller, which uses measurements done by induction loops to update the signal phase and timings plan. The intersection is also a part of a field experiment where freight traffic is granted priority based on vehicle connectivity…

With the steadily increasing road traffic demand, congestion on freeways has become a major problem. With the development of communication technology and automated vehicles, there are more opportunities for DTMs to be studied extensively at the coordination level and cooperation level. In the existing literature, the coordination of RM (ramp metering) + VSL (variable speed limit) + RG(route guidance) has rarely been studied. Besides, some coordination studies have limited discussion on the interaction between DTMs in non-coordinated cases. Based on these research gaps, the research question of this thesis is: What is the impact of coordinating RM, VSL and RG on a road structure where they have potential counter-effects on each other when following local objectives?

This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.

The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.

With the development of Autonomous Vehicles (AVs), a promising future for their implementation becomes increasingly apparent. However, it is essential to acknowledge that the effects of AV deployment are not straightforward, particularly when considering scenarios involving AVs from different manufacturers and various levels of automation. The current research predominantly concentrates on human-driven vehicles(HDVs) and AVs. However, the assumption that AVs exhibit homogeneous behavior is a simplification that does not reflect the actual diversity within this category.

This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs.

Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors.

To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels.

The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates.
Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts.

Car-following behaviour is a fundamental element for vehicle manoeuvre. The heterogeneity among driving behaviour has gained significant importance since some researchers argued that it might be relevant to capacity drop and traffic oscillations. Driver space is an area around a vehicle, and drivers will feel a rapid increase in discomfort when their comfort boundary is intruded. The response intensity of discomfort caused by spatial intrusion is represented by proximity resistance. With the development of Advanced Driver Assistance Systems (ADAS), it is crucial for drivers to feel comfortable within the implemented system. Therefore, understanding the heterogeneity of driver space has enormous potential to develop better customised ADAS. This report aims to analyse the impacts of heterogeneity on proximity resistance in car-following. The HighD dataset was processed to differentiate car-following driving styles for cars and trucks through the k-means clustering methodology. After successfully inferring the proximity resistance for each driver at each frame, the impacts of heterogeneity on proximity resistance in terms of different traffic states, vehicle types and driving styles were analysed. The results show that these three heterogeneities have different extent impacts on the proximity resistance of different drivers in car following. This study is beneficial for further investigations on the potential reasons for heterogeneous proximity resistance and the development of personalised car-following models.

New applications of connectivity between vehicles and the infrastructure are developed. One of these applications is providing upstream information on variable speed limits to connected and automated vehicles. It is expected that the connectivity can contribute to safer roads due to better compliance to the posted speed limits. Those expectations are based on microscopic models with the assumption that human drivers behave similarly in mixed traffic as they do in only human driven vehicles traffic. However, few studies have shown that human drivers tend to change their driving behavior when interacting with automated vehicles in mixed traffic. For this reason, a driving simulator experiment is executed to investigate the effect of the penetration rate of connected and automated vehicles and the distance at which the information is provided on the driving behavior of human drivers. The driving behavior was analyzed in terms of longitudinal and lateral behavior in the context of a three-lane motorway. The penetration rate was found to only impact the speed adaptation when combined with a large distance of upstream information. Lower means speeds, lower section entry speeds and increased speed compliance was observed with an increasing level of penetration rate. For the effect of distance of upstream information, a similar effect was observed. When the distance was increased the mean speed lowered, section entry speed lowered, and speed compliance increased. No change was observed regarding the lateral behavior or Time Headway. As a result, it can be concluded that the cooperation between connected and automated vehicles and variable speed limits on motorways can be used to slow down unconnected vehicles more upstream, without inducing aggressive driving behavior in terms Time Headway and lane changing behavior.

This research is carried out to determine the effect of truck platooning on traffic flow using empirical data. This research contains two parts, data fusion, and statistical analysis. For data fusion, loop detector data, infrastructure information and weather data will be added to the original data set. For statistical analysis, the time gap distributions under different categories are analyzed to determine the performance of the truck platoon. Additionally, an analysis of the lane change behavior is included.

The research of connected automated vehicles (CAVs) is an emerging topic within the field of transport & planning. It is not a question of whether the vehicles will be available for commercial use, but rather a question of when they will arrive. The safety of these vehicles is a necessary and ongoing discussion. In current research, a consensus is reached that crashes occurmainly due to human error. This human error is either due to negligence of the driving activity, like drunk driving or texting while driving, or due to incorrect decision making at critical moments. This study focuses on the topic of traffic safety with the principle of herd immunity in mind. It is theorised that crash risk can behave similarly to how a virus behaves (where crash risk is the chance that a crash occurs at a certain point in time). It spreads and infects vulnerable members of the population. The aim of this study is to determine whether the principle of herd immunity can be applied to car traffic, and if so, to what extent they can be compared through an impact assessment. The research question attached to this is: "How is traffic safety influenced by connected (automated) vehicles considering the concept of herd immunity?"

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.

In the future, Human Driven Vehicles (HDV's) are expected to interact with Automated Vehicles (AV’s) and Connected AV’s (CAV’s). Due to the differences in the expected driving behavior of AV’s (ACC) and CAV’s (CACC) compared to HDV’s, the nature of traffic breakdown phenomena in the future can be expected to change. AV’s (ACC) and CAV’s (CACC) refer to AV’s enabled with Adaptive Cruise Control functionality and CAV’s enabled with Co-operative ACC functionality respectively. Currently, there are traffic management measures which address traffic breakdown for the current situation. With the expected changes in breakdown phenomena in the future, will the current measures be effective in addressing the different nature of breakdown in the future? This research answered this question through simulation (Vissim), by focusing into the effectiveness of one of the current measures. The current measure whose effectiveness was analyzed is Variable Speed limits (VSL) applied through the concept of feedback Mainstream Traffic Flow Control, MTFC-VSL, at on-ramp merge sections. Before conducting simulations, it was hypothesized that MTFC-VSL control effectiveness in addressing traffic breakdown increases as CAV’s (with ACC and CACC functionality) penetration rate increases in mixed traffic, because CAV’s can be expected to precisely follow the speed limits. Mixed traffic in this research comprised of HDV’s and CAV’s (with ACC and CACC functionality). CAV’s (with ACC and CACC functionality) implies that CAV’s majorly differ with that of HDV’s in car following behavior and not in lane change behavior. Simulation results and analysis revealed that the hypothesis doesn’t hold good. Improvements in average Travel Time (TT) of mainline vehicles and average network speed due to the presence of MTFC-VSL control compared to the absence of it, deteriorated as penetration rate of CAV’s (with ACC and CACC functionality) increased until 20% in mixed traffic. For further penetration rates the improvements fluctuates. On-ramp vehicles for most of the scenarios of mixed traffic, are better off without MTFC-VSL control as the presence of it increases the vehicles average TT. MTFC-VSL control doesn’t effectively address the capacity drop phenomenon for various scenarios of mixed traffic. Lastly, it was found that for less than 20% CAV’s (with ACC and CACC functionality) penetration rate in mixed traffic, MTFC-VSL control effectiveness can be expected to overall increase if Intelligent Speed Adaptation is installed as an On-Board Unit in HDV’s, as it limits HDV’s exceeding the speed limits.It must be noted that, MTFC-VSL control was set up considering the practical considerations of implementing in real life, which can also be expected to play a significant role in hypothesis not being valid. Given the ineffectiveness of MTFC-VSL control for various scenarios of mixed traffic, the future traffic management measures should focus on the causes of breakdown phenomena which aren’t addressed by MTFC-VSL control. One of the proposed measures is a combination of a merging assistant strategy & MTFC-VSL control to better address traffic breakdown than MTFC-VSL alone. Merging assistant strategy utilizes the connectivity feature of CAV’s to foster smoother merging of on-ramp vehicles which isn’t addressed by MTFC-VSL control.

This study aims to quantify the impact on the traffic flow performance of different regulation strategies for a centralised route guidance system where road authorities and service providers work together in a coordinated approach. Previous research concentrates on the effect of a centralised route guidance system when every vehicle participates and all vehicles have perfect knowledge of the traffic state. This is not the real case with human drivers and multiple service providers and the impact of cooperation may be limited. This study combines habitual driving behaviour, the effect of the quality of information and a congestion avoiding user optimum algorithm to quantify the impact of a centralised route guidance system. The congestion avoiding user optimum algorithm will add a perceived time penalty to all routes with links above a certain intensity/capacity ratio to avoid choosing the congested route. The cooperation is described by the coordinated approach model of the SOCRATES²·⁰ project. In this model, the cooperation is organised by an intermediary who takes on the management tasks. Because a lack of commitment could be a problem for the success of the system, the services of the intermediary can be regulated with four regulation strategies starting with no regulation to regulation for both service providers and road users. The impact is determined with the dynamic macroscopic traffic model MARPLE. The result shows that without commitment the system does not improve the traffic state. For the maximum potential of the system, it must be fully regulated for both service providers and road users. Although with only the commitment of service providers, there is already a positive impact on the traffic flow and in less complex networks it can already solve all congestion.

Interacting with cyclists is a difficult challenge for Automated Vehicles (AVs). It is important that the introduction of AVs does not lead to a decrease in cyclist safety. This study presents a new methodology that is developed to assess road safety between AVs and cyclists. Surrogate Measures of Safety are identified specifically for the interaction between AVs and cyclists. The methodology makes use of the presented Surrogate Measures of Safety to objectify road safety, by classifying each into three different safety levels. The methodology is presented in a rubric that can be used to assess road safety of conflict situations. The methodology is demonstrated for a case-study in the Netherlands, analyzing 163.5 km of road with a total of 118 conflict situations. For each conflict situation, road safety is assessed and accident risk factors are identified. Common accident risk factors found are reduced visibility of a cyclists’ trajectory path, unclear infrastructure due to missing road surface markings, and infrastructure facilitating visual communication and occasional cyclist traffic violations. Although safety primarily needs to be on-board of AVs, road authorities can improve road safety by providing clear and predictable infrastructure, with good visibility of conflict situations and cyclists’ trajectory paths. Future research should focus on improving the performance of cyclist detection, classification, and trajectory prediction by AVs. Potential conflict situations between AVs and cyclists should be analyzed in more detail to quantify their safety level, and AVs should be trained in conflict situations where cyclists can be encountered.

Significant developments in the field of Advanced Driver Assistance Systems (A prove traffic management and prediction, driver comfort, and vehicle safety. However, a detailed knowledge of the way vehicles move is crucial for these applications. The mobility behavior has also changed over the last decades significantly. Both individual mobility and the mobility of goods is nowadays an essential part of our economy. The latest trends in vehicle automation are the predicted first signs of a partially/autonomous future of mobility. Whether this
future will be with or without individual vehicle ownership will become apparent in the coming years. For the moment, all researchers can do is to work on new topics tomake the future of mobility faster, more efficient, quieter, and safer. Detailed knowledge of state-of-the-art automation systems is essential in order to gain further insights into their effects, e.g., on traffic, the travel behavior of people, the security gaps caused by these systems, and passenger comfort.

To intrigue more people to cycle, it is essential to assess the current design of the cycling infrastructure to guarantee the efficiency and safety for the users. A credible simulation of cyclists is helpful to evaluate different designs of infrastructure and point out the problem. A valid simulation model requires proper verification and validation. However, there was hitherto no standard procedure to conduct model verification, and limited studies were found related to the verification of cyclist simulation models. To fill this gap, this study develops a verification framework for traffic simulation models based on the methodologies used in the literature. It also briefly discusses the difference to verify a cyclist simulation model. The framework is assessed by applying to a case study of the cyclist-queue-formation simulation model. The results prove the applicability of the framework to verify a cyclist simulation model. The organised structure of the framework provides a systematic measure for the verification of traffic simulation models. Furthermore, the cyclist-queue-formation simulation model is verified within the study area, where its conceptual model is proposed. The room for improvement of the simulation model regarding the relatively slow correction behaviour in steering and pedalling of the cyclists was also indicated.

This thesis provides an insight into the role of dockless shared-bikes (Mobike) in Delft. The objective of is to fill the knowledge gap about the users of shared-bicycles and their motivations to use shared-bikes in Delft. A survey is and finally gained 91 useful results. 76% of respondents are students, and in general, people are highly educated. 46% of respondents are commuters who work/study in Delft, 5% are tourists, and the other half are living in Delft. Most of the Delft’s inhabitants also own a bike; they are classified as ‘casual’, occasional Mobike users with the main reason to use Mobike as a temporary replacement for their own bike. Most of the commuters, who live outside of Delft, do not own a bike in and use Mobike every (week)day. Their main motivator to use Mobike is out of convenience. All this information finally leads to the main conclusion about the role of dockless shared-bike in sustainable mobility in Delft. From this research, it turned out that 98% of Mobike trips is replacing trips made with other sustainable transport modes such as walking, other forms of cycling and public transport.

With the increasing use of big data in varied applications to improve decision making and provide new insights, the research explores the potential of the Uber Movement data set released by Uber comprising of travel times from one zone to the other. A better understanding of the potential of the dataset could lead to the addition of the existing tool kit of Transport planners and city officials at the municipality of Amsterdam. Moreover, it would be the first of a kind data set enabling an understanding of taxi movement in the city. The Uber Movement Travel Time comprises of the average travel time between two wijken, where the ‘sourceid’ and ‘dstid’ do not correspond to the origin and destination of a trip but simply represent the directionality of the travel time measured. The data is aggregated across different levels of temporal detail and the number of data points directly corresponds to the level of temporal aggregation. For instance, if the quarterly aggregated data for the different days of the week is downloaded, the number of data points between a ‘sourceid’ and ‘dstid’ cannot exceed seven.
Three aspects of the data set were explored: 1) ability to capture the demand for Ubers 2) ability to capture recurrent congestion and 3) ability to capture non-recurrent congestion. While the data according to the Uber Movement and previously used instances, the data is suited for performance (recurrent congestion and non-recurrent congestion) and impact-related studies of the network. The absence of route related information limits the applications of the data. The potential of the data is also limited by the data sparsity. The potential of the data was best revealed through demand studies which indicated a skewed user group of tourists, airport users (to and fro), work-related trips and users using Ubers late at night. In addition, with respect to the goals of the municipality in managing traffic activity across different zones and time periods, by implementing and extending an existing model in the form of adding ‘occupancy related measures’ and ‘shortest path’. Thus, based on the data penetration levels and travel time data, the model developed offers insights at a strategic level to the city in the form of Spatio-temporal concentration of Uber vehicles, occupancy levels through the day. The potential of the data lies in its ability to offer strategic insights to the city of Amsterdam and the greater Amsterdam region in the form of the unique Spatio-temporal spread of Uber vehicles across different hours of the day.

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.