The field of automated vehicles gains more and more attention each year as the effective implementation of such vehicles in real life is imminent. The increasing amount of scientific studies addressing the impact of automated vehicles on the traffic network shows the same trend. While much research focuses on regular intersections, only a few investigate the impact of turbo-roundabouts. In particular, studies researching the obtainable capacity increase on turbo-roundabouts under fully automated conditions are scarce. A turbo-roundabout is a multi-lane roundabout with separated lanes that require vehicles to choose their desired destination before entering the turbo-roundabout, effectively eliminating most weaving conflicts on the roundabout itself. Only a single paper researches how much additional merging capacity can be obtained by automated vehicles on a turbo-roundabout compared to human-driven vehicles (Fortuijn and Salomons, 2020). This study takes a theoretical approach and uses existing theory about turbo-roundabout capacity to generate five graphs depicting the merging capacity of the left lane of a minor access branch for varying circulating traffic volumes on the turbo-roundabout: four models for differing automated vehicle settings and one human-driven vehicle model. These four automated vehicle models differ in complexity, where the most complex model features gap synchronisation, headway optimisation, and path guidance. Each model, therefore, has different following distances, vehicle speeds, critical gaps, etc. The results of the theoretical models are capacity curves depicting the merging traffic capacity over the circulating traffic volumes.
This thesis investigates whether the capacity increase obtained from the most simplified automated vehicle model of the theoretical study can be obtained through microscopic traffic simulation in VISSIM. What must be noted is that the most simplified model of this theoretical study features communication between vehicles, which is not the case in the simulation models of this thesis. Therefore, higher follow-up times must be maintained in the simulation models, whereas the theoretical model uses minimal follow-up times to obtain a capacity increase of 58%. Initially, this thesis aimed to determine the achievable capacity increase for Connected and Automated Vehicles. However, due to limitations in VISSIM, the capacity increase could only be obtained for Automated Vehicles as the software cannot model communication between vehicles. Therefore, this report does not obtain the highest potential capacity increase for automated vehicles but serves as a stepping stone for more complex AV models. It determines the additional merging capacity of automated vehicles on turbo-roundabouts, only considering key parameters such as the car-following behaviour, critical gap and follow-up times. Gap synchronisation, an important aspect of the study of Fortuijn and Salomons, is not included in the simulation models due to shortcomings in VISSIM. Before making the AV models, this thesis aims to calibrate the VISSIM model for human drivers with real traffic data at a turbo-roundabout in Nieuwerkerk aan den IJssel, the Netherlands. Only when the VISSIM model is capable of accurately modelling human behaviour can it be used to determine the capacity increase through AVs. The thesis starts with developing a human-driven vehicle model to determine the merging capacity of the turbo-roundabout in current traffic conditions. The calibration is conducted on the traffic flow level through an iterative process where the critical gap and follow-up times are adjusted. The root-mean-normalised-squared error (RMNSE) determines the difference between regression lines of the merging capacities of the simulation model and the real traffic measurements. Based on the calculated differences between both lines, the critical gap and follow-up time parameters are adjusted.
For the left lane, the iteration process reduced the RMNSE from 0.427 to 0.072, equaling an improvement of 72.5%. For the right lane, the iteration procedure gave an unsatisfactory result. The RMNSE increased from 0.408 to 0.427, which signifies an increase of 4.7%. The final capacity curve after seven iterations was unrealistic. It was concluded that the VISSIM model performs adequately at simulating realistic behaviour on the left lane but that the same accuracy could not be achieved for the right lane. The calibration procedure is only applied to the minor branch of the turbo-roundabout as the data for the major branch could not be used for calibration. Subsequently, the calibrated model serves as the basis for developing three automated vehicle models: cautious, normal, and aggressive, each representing a different aggression level of automated vehicles. Each of these models features a different car-following model to simulate the various levels of aggression. Furthermore, the critical gap times of the human model are changed to model Avs properly, and for each of the three models, a penetration rate of 100% of AVs is applied. For each of the three automated vehicle models, this thesis measures the increase in capacity. It compares it to the theoretically obtained capacity increase of 58% for the left lane of the minor access branch of the turbo-roundabout. This thesis finds capacity increases of 7.47%, 38.62%, and 24.32% for the cautious, normal, and aggressive automated vehicle models, respectively. For the right lane, the obtained capacity increases were not calculated as the final HDV model is inaccurate for this lane. An unexpected result is that the normal automated vehicle model outperforms the aggressive model for the left lane, even though the parameter settings should result in a higher capacity for the aggressive model. Comparing the resulting capacity increases to the 58% increase of Fortuijn and Salomons, one can conclude that the simulation model does not reach the same increase in capacity as the theoretical model. This is not an unexpected result, as a theoretical simulation model has fewer interactions, and the theoretical model assumes communication between vehicles, whereas the simulation models do not. Therefore, concluding that the microscopic traffic simulation models in VISSIM cannot accurately model the behaviour of automated vehicles is premature. The models leave room for improvement by fine-tuning various parameters that were ignored in this thesis. This thesis concludes that, while VISSIM is adequate at simulating human-driven vehicles, it has various limitations for modelling automated vehicles. First of all, gap synchronisation and headway optimisation cannot be modelled, while these are the most critical elements to unlock the full potential of automated vehicles on turbo-roundabouts. If one wants to incorporate these into VISSIM, an external mathematical software package is necessary. Considering other software packages for studying (C)AVs on turbo-roundabouts is advised. This thesis is a foundation for future research on turbo-roundabouts, both for the calibration process and for simulating automated vehicles.

This thesis aims to answer the question "What are the properties of a resilient road network?" The resilience of a road network indicates the magnitude and consequences of a disruption, which can be events such as a traffic accident or a flood. The literature is not in agreement about the exact definition of road network resilience, and the way to quantify it. A literature search is done to look for definitions and resilience metrics. Eleven metrics are found that quantify resilience in road networks to accidents. These eleven metrics are compared based on whether they are on a network level, and how many variables are used. Two metrics are chosen, one based on travel time, and one based on space-mean flow. Another metric, based on the outflow of the network is added.

The three chosen metrics are then compared in small networks to see if they give the same results. The small network consist of five nodes with different link configurations. The conclusion about which network is the most resilient changes with the resilience metrics. The metric based on travel time is deemed the best for this type of research. The metric based on network outflow can have distorted results due to high peaks, and the metric based on space-mean flow is better suited for a different experiment design.

To see how network parameters such as link density influence the resilience, a simulation with random networks is done. 266 networks with nine nodes in random locations, and random links between them are simulated. It is concluded that networks with a lower link density have a higher resilience. This is not only illustrated by the relation between resilience and link density, but also by several other network parameters which are related to link density such as average number of lanes and connectivity. A reason that these networks are more resilient could be that there is less spillback, the links in the high density networks are longer and have more lanes, so congestion will not spread to other links as fast.

In the face of urbanization and limited space, traffic management grapples with challenges, especially heavy left-turn volumes at signalized intersections. The pre-signal strategy, proposed by Li et al. (2014), emerges as a promising solution. It involves an additional traffic light upstream of the main intersection, creating a "sorted area" where vehicles distribute before reaching the main intersection. Although simulation studies show increased intersection capacity with pre-signals, behavioral assumptions about drivers' spread behavior in the sorted area lack validation from behavioral science.

This study investigates drivers' behavior in pre-signalized intersections, addressing how their lane selection downstream of pre-signals impacts efficacy. The main research question is: How does drivers' lane selection downstream of pre-signals impact the efficacy of pre-signal implementation for regular traffic? A stated-choice experiment involved over 1000 respondents making preferred lane choices in diverse traffic conditions. Analysis using a MultiNominal Logit (MNL) choice model reveals crucial insights.

Results indicate that pre-signals don't alter drivers' perceptions of traffic crowdedness. However, discomfort with lane changes emerges as a critical factor influencing efficiency. Drivers strongly hesitate to execute multiple lane changes, posing challenges for achieving an equal spread in the sorted area.

Two implementation options are discussed: using the entire sorted area for all traffic or dedicating parts to specific traffic directions. Each option has efficiency and safety trade-offs. Designing a pre-signal system achieving both an equal spread and using the entire sorted area for all traffic proves unfeasible.

The study emphasizes integrating behavioral science into pre-signal studies, offering valuable insights for traffic engineers. It lays the groundwork for understanding pre-signals, highlighting the imperative for continued research and a holistic approach to their implementation.

Adaptive Cruise Control (ACC) relieves human drivers’ tasks by taking over the control of the throttle and braking of the vehicles automatically. However, it has been demonstrated in many empirical studies that current production ACC systems fail to guarantee string stability. It is believed that if vehicles can take the longitudinal dynamics further downstream into account and react to the propagating disturbance earlier, the string stability in the platoon may be improved. Instead of relying on inter-vehicle communication technologies, the ego-vehicle should be able to detect the second leading vehicle by leveraging the power of on-board sensors. Still, the second leader measurements can be highly erroneous. Therefore, it is important to consider the entailed measurement uncertainties when designing and evaluating such ACC systems. This study proposes several ACC systems which possess the property of multi-anticipation and uncertainty handling.

The possible sensor technology which can collect the second leader measurements is first investigated. Based on the considered setup, the measurement uncertainties are modelled to reflect the real-world conditions. The ACC system architecture and control system design method are then proposed. Deep reinforcement learning is applied for the controller design in light of its great potential in describing the complex non-linear control task and handling the uncertainties. Kalman filters and recurrent policies with a Long-Short-Term-Memory network are applied to cope with uncertain measurements. The first method estimates the state information before feeding it back to the controller agent, while the latter incorporates the state estimator into the controller to actively consider the uncertainties while making decisions.

A numerical simulation approach is adopted to theoretically assess the performance of the proposed ACC systems. A traffic disturbance event and multiple levels of measurement noise are considered in the experiment. To analyze the performance in terms of string stability and ride comfort and understand the car-following behavior mechanism resulted from the proposed systems, a quantitative analysis framework is developed.

The evaluation results demonstrate the applied learning-based approach succeeds to train ACC control policies which can ensure string stability. It is also found that the multi-anticipation ability significantly improves the string stability and ride comfort performance. In the scenarios with measurement noise, systems using the tuned Kalman filters exhibit the ideal level of string stability performance. However, ride comfort cannot be guaranteed in scenarios with large measurement noise. On the other hand, systems using recurrent policies can better ensure ride comfort performance while maintaining string stability at certain levels. Based on the results, the performance limits of the proposed ACC systems in the handling of measurement uncertainties are explored. In addition, with the different policy training setups, the trade-off between these two performance aspects is shown.

The findings of this study are anticipated to trigger the development of advanced multi-leader ACC system by automakers, sensor manufacturers, and traffic engineers. Future work can be directed to an enhanced controller design. Robustness of the systems with respect to other sources of measurement uncertainties, more types of traffic disturbance, and platoon heterogeneity is worth further design consideration and analysis.

One of the current challenges withholding personalised lane-level driving advice is the inaccuracy and error of GPS signal from commonly used navigation devices and mobile phones. These GPS signals have an uncertainty margin up to several meters, therefore potentially indicating the vehicle location on a different lane than the actual lane it would be in. This unreliability therefore currently makes it impossible to accurately recognise lane changes from solely this data. This study looks into the recognition of lane changes from only Floating Car Data by the use of a Random Forest algorithm. In order to find the ground truth, a trajectory reconstruction algorithm is implemented, which uses the matching of trajectories with loop detector passages in order to find the lane a vehicle is in at each loop detector location. This information is then used to know whether, for each vehicle, a lane change is made on the road section in-between two consecutive loop detector locations. By training the model on this data, it was found that when using solely Floating Car Data, lane changes can be recognised with an accuracy of up to 64%. Indicators for lane change were found to be the lateral distance of a vehicle to the middle of the road, as well as the heading of the vehicle. The study additionally looks into a rule based method of lane change recognition, which is compared with the Random Forest model.

The traffic density of small aerial vehicles operating within urban environments is expected to increase significantly in the near future. This urban environment is highly constrained due to being limited to the low-altitude airspace directly above the existing road network. To increase understanding of the factors influencing the capacity of urban airspace, multiple empirical studies have been performed using fast-time simulations. However, the empirical nature of these simulations hampers the extrapolation of their results beyond the specific conditions that have been tested. At the same time, the emergent behaviour of aircraft in constrained urban airspace, such as queueing and local hotspots, yields the existing analytical models for general airspace invalid. In this paper, we derive and validate an analytical model approximating the relationship between the mean flow rate and mean density in a two-dimensional orthogonal grid network airspace, expressed in the so-called Macroscopic Fundamental Diagram. This analytical model is based on road transportation queueing theory and can explain the different interactions occurring in constrained urban airspace compared to general airspace. Notable findings show that the entire airspace can become unstable when the maximum capacity of a single intersection only is reached. Furthermore, the maximum airspace density is found to be unaffected by cruise speed. The results demonstrate how the derived analytical model for the Macroscopic Fundamental Diagram provides an increased understanding of the factors that influence capacity of constrained urban airspace, thereby offering an effective tool for urban airspace design applications. Moreover, this model lays the groundwork for the derivation of more expanded models, including the altitude dimension and non-orthogonal or non-four-way intersections, that can further improve comprehension of constrained urban airspace.

This paper aims to explore the potential of mobility hubs to reduce car use and car ownership, and how this is affected by the car owner’s characteristics, the trip characteristics and the car owner’s living environment characteristics. To this end, two stated choice experiments were conducted and the data was analysed using extended multinomial logit models. The results showed that the shared e-car is preferred to the private car for commuting trips if the walking times and operational costs are attractive. The operational costs of shared mobility services and the walking distances to mobility hubs were found to be important factors for the use of shared mobility services. These two factors were also found to be important for the willingness to give up the private car and thus the choice for shared mobility services offered in mobility hubs for all trips. Sensitivity to operational costs was found to be higher for the shared e-car than for the shared e-moped and e-bike. Car owners preferred full e-car sharing if the private car was given up. Car owners had little preference for the use of shared e-mopeds and e-bikes. The density of mobility hubs in a residential area was found to have a strong impact on reducing car use and car ownership. Moreover, lowering the current operating costs was found to make the use of shared mobility services more attractive. The effects of mobility hubs on car use and car ownership were highest in the city centre, followed by the suburban area and lowest in a rural area.

In order to reduce congestion numbers on the highways, ramp metering can be used. The currently existing ramp metering control strategies are of a macroscopic nature. This means that the average occupancy on the main lane or average flow values on the main lane determine the temporarily fixed cycle times for the traffic light located near the on-ramp to control the inflow from the on-ramp. In this thesis, it is investigated to what extent a microscopic approach to ramp metering would lead to less travel time delays. This approach shows a green light for the on-ramp vehicles when a gap in the main lane flow on the right lane has been detected instead of using the temporarily fixed cycle times. Computations on the location of the gap measurement loop detectors is required. Therefore, an acceleration distribution has been composed by means of an experiment as well. Concludingly, it was found that the proposed microscopic algorithm leads to additional travel time savings in the base case scenario compared to the currently used Rijkswaterstaat algorithm. Furthermore, adjustments to the developed microscopic control structure, such as a combined Rijkswaterstaat and microscopic algorithm, are proposed and it is recommended for further research to implement these adjustment and investigate their effect on the travel time delays. (PS. The paper was included at the end)

The aim of this master thesis is to develop a dynamic zone-based traffic model for large areas, using the concept of the Network Fundamental Diagram (NFD), which relates the average internal flow (production) and speed in a zone to the number of vehicles in the same zone (accumulation). The model that has been developed is then applied to the Randstad area, and it is analyzed whether the model is able to reproduce the observed congestion patterns and travel times between the zones.

The Dutch governmental organisation Rijkswaterstaat contributes to the smooth and safe flow of traffic, as both traffic jams and accidents cost society large amounts of money each day. Roads are designed for the current traffic composition. Due to the promotion of Adaptive Cruise Control (ACC) systems, utilisation of these systems is expected to increase. Society benefits from insights into the effects these systems have on traffic flow, as they can help to reduce traffic jams and accidents. ACC systems are designed to increase driving comfort by taking over throttling and braking from the human driver. For optimal driver acceptance, these systems show similar driving behaviour to that of human drivers. However, this is not entirely possible due to limited anticipation. To predict how differences in driving behaviour affect traffic flows, researchers usually perform simulations using parametric car-following models. However, research shows contradictory findings. The goal of this research was to gain insights into the performance of commonly applied parametric car-following models on representing the driving behaviour of ACC systems. Optimal model calibration was obtained by investigating the sensitivity of the model calibration to synthetic data. Investigated were the calibration methodology and the quality and quantity of calibration data. Models are calibrated to real-world driving data from an Audi A4 from 2017. These models were used to assess the capability of representing typical highway scenarios: steady-state car-following, cut-in, cut-out, hard-braking and stop-and-go scenarios. The considered models were the Intelligent Driver Model (IDM) model, which has previously been applied to model the driving behaviour of human drivers, the newly developed simplified ACC (sACC) model and a variant on this model. Insights in the sensitivity of the model calibration were obtained by performing a sensitivity analysis on synthetic data. Essential factors in achieving an optimal model calibration are: 1) the model closely matches the driving behaviour in the data, 2) noise levels are as low as possible and 3) the data should contain as many situations as possible that are also included in the model. The dataset must be sufficiently long to include all these situations and to allow the model to develop its dynamics entirely. Using these insights, a calibration was performed on real-world ACC driving data from an Audi A4 (2017). For the ACC system, it was found: 1) the ACC system exhibits non-linear driving behaviour, 2) the acceleration depends on the current velocity and distance to the desired velocity, 3) the system does not consider an intelligent braking strategy and is thus not able of handling safety-critical driving situations and 4) the model includes a sub-controller which ensures comfortable driving behaviour. Except for the comfortable sub-controller, the non-linear IDM model considers all of these factors and thus best represents the driving behaviour. The linear sACC model cannot represent standing conditions, which is resolved in the alternative version. The linearity allows for a better representation of the behaviour of the comfortable sub-controller. However, it disallows for an accurate representation of the dynamics by the models.

There is a need to know flow capacities of common bottlenecks of cycle storages because an increasing number of large cycle storages are being designed. Flow capacity values are not available from literature nor is a suitable method to determine those values. This research contributes to filling these research gaps, by developing a method to measure capacity, and applying it to case studies. The method also offers a way to calculate the standard deviation of the flow, as well as a confidence interval for the capacity. This is possible because the flow is normally distributed. Knowing the variation of the flow, predictions can be made on how often a queue will emerge. The essence of the method is to measure in an event-based manner, discard the time at which there was no queue before the bottleneck, and then divide the remaining measurements into intervals of equal duration and treat those intervals as individual capacity measurements. The method has shown to yield a capacity value that is independent of the averaging interval. The standard deviation however, does not always vary as independent data from a normal distribution would. It is hypothesised that the autocorrelation could be used to correct for that. 95% confidence intervals for capacity were found as follows; check-in: 21.2 ± 1.2 (σ=3.5), stairs: 24.78 ±0.60 (σ=1.02).

Freight transporters use software kits to plan the routes for their trucks. Breaks required by the European drivers legislation are nowadays planned as late as possible. However, in some cases it is beneficial to plan these mandatory breaks during waiting time, such as truck driving bans. In this thesis this problem is addressed by computing earliest arrival routes with optimal break planning. The problem is formulated and optimally solved as a variation of Dijkstra's algorithm. The slow Dijkstra computation times of several seconds per route are improved using time-dependent contraction hierachies, which enable a query time of several milliseconds per route while the solution quality remains good. For two days of driving with a night rest in between, 17% of the analysed routes improves with optimal break scheduling, resulting in an average improvement of 5 hours of driving time.

If the taking of breaks is additionally restricted to parking lots, the influence on the arrival time is on average increased with only 3 minutes. However, 5% of the considered routes are not feasible any more due to absence of truck parking lots along the planned route. Another 15% of the routes face large changes in roads that should be taken.

The planned routes are all optimized for having the earliest arrival time. However, the freight company's objective also concerns fuel optimized driving or providing reliable arrival times to the customer. This thesis analyses such preferences and combines them into a route planner that incorporates results of stated and revealed choice experiments. It is shown that generally toll avoidance and congestion avoidance have most influence on the route choice and arrival time. On average differences are small, but for some routes this leads to changes of up to 15% in travel time.

This thesis analyses the problem of route planning from two perspectives: algorithmic and behavioural. Several observations are made: algorithms are not suitable for obtaining the best route instead of the fastest, and the output of behavioural research cannot be used directly in practice to compute preferred routes. Effort could be made in future to integrate both research communities such that algorithms are able to reflect what a freight transporter actually wants.

Met de ontwikkelde datafusiemethode, die snelheden met gewogen intensiteiten fuseert, is het mogelijk om in plaats van Fundamentele Diagrammen op snelwegen en Macroscopisch Fundamentele Diagrammen voor hele netwerken, ook op traject gebaseerde Macroscopisch Fundamentele Diagrammen door complex stedelijk gebied te verkrijgen. Met een traject specifiek gefit Macroscopisch Fundamenteel Diagram is het schatten van missende informatie over zowel intensiteiten als reistijden vervolgens mogelijk wanneer de andere grootheid bekend is. Vooral voor reistijden leidt dit tot relatief goede schattingen. In de beroepspraktijk blijkt daarnaast ook dat vooral het hebben van informatie over reistijden erg waardevol wordt gevonden. Deze kunnen namelijk worden gebruikt voor de verkeerskundige prestatie-indicator “Level Of Service gebaseerd op gemiddelde reistijd per kilometer, met toegevoegde standaarddeviatie van de reistijden die langer dan gemiddeld duren”. Deze indicator is uit een ontwikkelde lijst van getoetste indicatoren als meest geschikte maatstaf aanbevolen voor de beroepspraktijk om de verkeerskundige prestaties van een stedelijk wegennetwerk op een eenduidige en communiceerbare manier weer te geven. Hiermee leiden de resultaten van het onderzoek tot een beter en completer inzicht van de verkeerskundige prestaties van een stedelijk wegennetwerk, om deze vervolgens op een eenduidige en communiceerbare manier weer te kunnen geven.

There is a high probability that communication between vehicles in a traffic network and between vehicles and traffic controllers will be the next major wave of technological innovation in traffic. The collection of data of communicating vehicles in traffic offers live information about the traffic conditions, which can be used to control traffic optimally. It is expected that in the coming years a new vehicle type will take part in traffic that is able to navigate through traffic without driver and communicates with traffic controllers to share its information and receive its optimal trajectory: the automated vehicle. The automated vehicle is different from the autonomous vehicle, which does not communicate with traffic controllers or other traffic.
The traffic mixture consisting of conventional, connected and automated vehicles will change gradually over time. Designing a traffic control strategy that performs well for traffic consisting of different shares of these three types of vehicles and requires only a limited amount of computational power will be a main challenge in traffic control the coming years.
This report proposes a traffic control strategy and assesses the influences of this strategy on the network wide traffic conditions using a traffic simulation. In this way, the research will provide insight in the effects of vehicle connectivity and automation on the network wide traffic conditions.

Based on the live traffic information from the communicating vehicles in a network, traffic can be controlled from two sides: the traffic signal control and the automated vehicle trajectory control. The traffic control method proposed in this report works as follows: first, the traffic controller collects all the information of connected and automated vehicles in the network to update the vehicle set. Locations and speeds of connected and automated vehicles can be obtained directly. Automated vehicles also provide the traffic controller of information of the non-communicating vehicles in their surroundings, detected by their sensors. Then, the virtual departure time of each vehicle will be determined, which is the time at which the vehicle would depart not hindered by other traffic or traffic signal and can be calculated using the kinematic laws. Also the corresponding virtual departure speed is determined for each vehicle. Then, starting from the first vehicle on the link, the expected departure time will be calculated, based on the virtual departure time and the traffic signal state. The expected departure times and speeds of the other vehicles on the link will be based on their virtual departure time, the expected departure time of the vehicles in front of the vehicle and the signal state. The virtual and expected departure times and speeds are used for both the signal control and the trajectory control.
This report presents a decentralized signal control to limit the computational power required in large traffic networks. This signal control is based on the original back-pressure algorithm, which compares the downstream traffic conditions with the upstream traffic conditions around an intersection and makes decisions for the signal control based on the difference in back-pressure of different movements on an intersection. The proposed signal control strategy takes the approaching and exiting vehicles within a certain range from the intersection into account. At the start of each time slot, a prediction is made for the pressure of a link on the intersection at the start of the next time slot, based on the current number of approaching and exiting vehicles on a link, their expected departure time from the intersection and the expected traffic demand. Based on the predicted pressures at every link that is connected with the intersection, the traffic controller decides whether it should switch the traffic signal at the start of the next time slot.
The proposed method for the trajectory control of the automated vehicles is based on the decisions of the traffic controller regarding the traffic signal and the expected departure times and speeds of the vehicles in the network. The main goal of the trajectory control is to use the green phases in an optimal way, i.e. maximizing the amount of departing vehicles per unit of green time. The trajectory control should limit accelerations and decelerations in the network as much as possible to limit emissions and traveller discomfort. Therefore, a third type of departure time and speed, the smooth departure time and speed, is introduced and compared with the expected time and speed of an automated vehicle to optimize its trajectory. The smooth departure time is the moment in time at which the vehicle would depart from the intersection if it would accelerate constantly to the expected departure speed over the total distance to the intersection. If the expected departure time is later in time than the smooth departure time, the vehicle will slow down and vice versa. If the smooth departure time is equal to the expected departure time, the vehicle will accelerate constantly towards the intersection.
The proposed control method is tested in a traffic simulation to obtain the effects of the method on the traffic conditions for different traffic scenarios. For each scenario the average traffic time delay, average green time, average and maximum queue length and average acceleration and acceleration time is retrieved from the simulation to assess the effects of the proposed methods on the traffic conditions. The simulated network consists of a two-directional main road with five signalized intersections with side roads. The traffic consists of only cars and other modes of transportation are neglected for simplicity. Different shares of connected and automated vehicles and different vehicle flows in the network are tested.

The simulation results show that the proposed signal control strategy leads to relatively short green times, which decrease the intersection throughput and the ability of adjusting the green time ratio between the two crossing roads to the traffic demand. Traffic on the main road encounters relatively longer green times and shorter queue lengths than traffic on the side roads. The throughput of the intersections does not satisfy the traffic demand and therefore, the first intersection will function as a bottleneck and lowers the traffic flow at the next intersections. Therefore, queues especially occur at the first intersection that vehicles encounter. The results show that the higher the penetration rate of communicating vehicles is, the better the traffic controller is able to adapt the signal sequence to the current traffic situation. The signal control requires several improvements in order to function optimally, but has the potency to be a suitable decentralized control method in a partly connected and automated environment.
Results regarding the proposed trajectory control show that a higher share of automated vehicles in the network results in a higher average acceleration in a relatively shorter total acceleration time, which is a result of the fluctuations in the vehicle speed due to the trajectory control. However, automated vehicles increase the average number of departing vehicles per unit of green time, which leads to shorter queues and less average travel time delay in the network.
Next to the proposed signal control strategy, an existing coordinated signal control strategy is implemented and tested in the simulation without connected or automated vehicles to compare the results with the proposed methods. Simulations of this green wave control result in larger average travel time delays and larger queue lengths due to relatively shorter green phases. The average acceleration, however, is relatively low, since the green wave strategy avoids vehicles to decelerate to complete standstill after they cross the first intersection of the traffic corridor.

This research shows that vehicle connectivity and automation offer the ability to improve network wide traffic conditions applying a decentralized traffic control method to control signalized intersections and automated vehicles. Although the proposed traffic control method is not optimized yet, it shows that low shares of connected and automated vehicles in traffic already make a significant difference in the network wide traffic conditions and that the signal sequences and automated vehicle trajectories can be even optimized if these shares get higher. Since the control strategy is decentralized, the required amount of computational power of a traffic controller is limited to the amount that the control of one single intersection requires and is not depending on the network size.
This research provides several recommendations for further research to improve the proposed strategy. Instead of taking only vehicles into account within a certain range from the intersection, which all have the same contribution to the back-pressure, an interesting alternative is to take all vehicles around an intersection into account for the back-pressure and add a certain weight to each vehicle based on the distance to the intersection. This weight can be used to determine the contribution of each vehicle to the back-pressure. Next, a threshold for switching and a dynamic slot time instead of a fixed one could be improvements to the proposed method and should be tested in further research.
The trajectory can be optimized especially regarding the vehicle accelerations and decelerations. It is recommended to avoid the occurring speed fluctuations in the proposed method by optimizing the acceleration rate that is used for automated vehicles to reach the desired trajectory.
It is recommended to test the proposed method in more realistic traffic simulations in further research to get more insight in the effects to real traffic. In that way the control method can be adapted to real traffic. The simulation of this research uses many assumptions regarding several parameters which require further research to optimize the performance of the control method. Last, before implementing the proposed method or a similar method in traffic, further research is required regarding the traffic safety. Especially the fact that automated vehicles are able to depart from intersections exactly at the moment the traffic signal switches to green could lead to unsafe situations, since other traffic users are not yet used to this behaviour of automated vehicles.