Subsea pipelines are critical for oil and gas production and transport, more than 100.000kmof subsea pipelines have been laid. These pipelines are laid by specially designed ships which can lay these pipelines at great depth and a high speed. However, designing the supply chain to deliver joints (pipe pieces) to these pipe lay vessels can be very complex; there aremany possible supply vessel combinations andweather influences that can impact the supply chain. This makes trying to find a good solution a very difficult and time-consuming process currently with a lot of manual iterations. This is called the Allseas problem. To solve this supply chain problema mathematical model is developed, thismodel can solve the supply chain problem while taking into account the many constraints. The research question that is discussed in this thesis is: Can the optimal usage of supply vessels, required to solve the pipelay vessel supply problem, be determined using a scenario based mathematical optimisation model? This model can eliminate the need for the many manual iterations and deliver an optimal solution that the Logistics and chartering department can use. To supply joints to the pipelay vessels several different types of supply vessels can be used. These supply vessels have different characteristics such as; hold capacity, sailing speed, capital- and operational expenditure, and workability (ability to work in bad weather). The supply vessels must supply joints to the pipelay vessel which can have one or two pipe transfer cranes. The number of pipe transfer cranes that can be used significantly influences the speed at which the supply vessels can be unloaded. A final important influence on the supply chain problemis the weather; specifically the wave height and wind speed: they determine if a supply vessel can unload it joints, or if the pipelay vessel can lay pipe. The goal of the problem is to design a fleet of supply vessels that can deliver the cheapest possible set of routes that will supply all the required joints to the pipelay vessel on time. A scenario based mathematical optimisation model is developed to find the solution to the Allseas problem. Some important parameters for this model that are thoroughly researched are: the desired stock level of the pipelay vessel, i.e. how full should the holds be? The demand window, i.e. how close to the demanded number of joints should the model deliver. The final parameter of interest is the node duration, all joints must be delivered during a certain time window the parameter node duration changes the size of the time window. The influence of these parameters is also investigated when weather is simulated. The results of the developed program show that it is possible to make a mathematical model that determines the optimal pipe supply vessel usage. Some other interesting parameters settings have also been found by the model. Allseas always tries to keep the pipelay vessels stocked as full as possible, however, the mathematical model has shown that it ismore advantageous to try to keep the vessel at 80%capacity sine the supply vessels can unload faster that way ensuring shorter routes. Furthermore, the mathematical model has shown that it can deliver cheaper solutions than are currently designed. This has been proven by comparing the results of the mathematical model with a project that Allseas has completed. The mathematical model presented a solution that was ¼ 10% cheaper than the solution Allseas implemented.

Dynamic pricing can be used for better fleet distribution in free-floating vehicle sharing (FFVS), and thus increase utilization and revenue for the provider by reducing the supply-demand asymmetry. Supply-demand asymmetry refers to the existence of an undersupply of vehicles at some locations at the same time as underutilization of vehicles at other locations. We propose to use dynamic pricing as an instrument to incentivize users to rebalance these vehicles from low demand locations to high demand locations. Despite significant research in rebalancing vehicle sharing, literature so far lacks experimental results on dynamic pricing in free-floating vehicle sharing. We propose to use an algorithm that minimizes the differences in the idle time of vehicles. The algorithm is tested in a real-life experiment that was conducted in cooperation with an FFVS provider. The experiment shows that even slight dierences in pricing and a novice algorithm can already influence user-behavior to counter the supply-demand asymmetry. Improving the existing algorithm by doing more experimental research is advised to further uncover the potential of this strategy.

This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the behaviour of a container terminal is unknown beforehand, costs can change over iterations. Therefore, Liu et al. [Liu et al., 2019] and Keselman et al. [Keselman et al., 2018] show the advantage of combining A* with Machine Learning. This way, the exploring part of the ML algorithm is combined with the fast andmore precise properties of the A* PP algorithm. This thesis has proposed the machine learning algorithm Vehicle Aware Reinforcement Learning Path Planning Algorithm VARLPPA. This algorithm uses Monte Carlo Control method. This is a model free approach, which has been shown in both experiments to find more efficient solutions in exceptional situations.

Supply chains are to be found in almost every corporation around the world. When tryingto manage a supply chain often two types of disturbance can jeopardize the effectiveness of a supply chain. These are supply and demand uncertainty. This thesis tries to investigate the effects of those two types of disturbances in a supply chain and seeks to find a remedy. When taking a closer look at supply chains, robust supply chains are considered to withstand a higher level of supply and demand uncertainty. Robustness knows many definitions but is mostly characterized by the responsiveness and adaptability of a supply chain. Responsiveness implies a degree of quick reaction to sudden changes, while adaptability is defined as the capacity to deal with new circumstances. This thesis tries to relate robustness according to a level of resistance of supply and demand uncertainties. The supply chain of the royal dutch land forces is prone to such sudden changes. Therefore this research incorporates the context of a military supply chain to perform experiments on. The military supply chain in operational areas is selected and identified as a 3-echelon-multiple tier-supply chain, where the implementation of a networked supply chain can result in an increase in robustness. The research uses three key performance indicators to ”measure” a degree of robustness. With the use of a multi-agent discrete-event model that incorporates a highly dynamic environment including real-time transportation and order handling simulations. The model also contains an nteger supply optimization module using CPLEX and a distance-based swarming algorithm to emulate the lifelike situation of the supply chain concerning order handling and ”Notice to move”. Three experimental setus have been created that try to quantify robustness levels according to networked supply chain operations while coping with demand and supply uncertainty. First, a sensitivity analysis is performed, revealing the basic relationships between the indicator and emand/supply fluctuation. Second, an extensive model testing according to three different load cases in uncertainty is performed. Thirdly a case study specified for a military context is done. It is found that robustness can be increased by increasing the amount of networked forward supply nodes and interesting dependencies are revealed between the amount of forward supply centers and robustness. The findings of this research can be applied to other industries that have a similar supply chain design.

Retailers must adapt their activity to comply with the evolving demands of their customers. Increasing demand on order-fulfilment services, along with determination to keep high service levels, have raised the problem of space scarcity at retailer stores to keep and process picked customer orders. Congestion is then experienced in store order-picking depots. A design proposition is given to overcome this problem for a top retailer’s store in the Netherlands. Using engineering design methodology, the design proposition is created with at least one short-term solution for each of the 3 stages of an item’s movements within the store: (1) incoming items; (2) storage and order-picking that includes storage at the sales SKU, job-sequencing, storage at the depot and storage during peak periods of activity; and (3) outgoing items. Long-term solutions are also given to look into that would enhance technology and prepare for the foreseeable future customer behaviour developments in purchasing methods. Empirical evidence is provided on different job-sequencing methods that extends order-picking theory on picker-to-parts systems; and further on causes for a more complex view of out-of-stock theory than from the perspective of a shopping customer at the store, instead focusing on out-of-stocks for paid items by an ordering customer.

The significant growth in B2C e-commerce in the last decade increased the traffic and volume of parcels in last-mile delivery significantly. To mitigate the impact of the last-mile delivery service such as the increased traffic and carbon dioxide (CO2) emission, crowdshipping emerged as an innovation that utilise the travelling crowd as occasional courier to deliver parcels. The goal of this study is to analyse the potential impact of crowdshipping performed by private cars to the local vehicle mileage and its result to the CO2 emission. This is done by simulating the passenger and parcel delivery transportation activities in agent-based modelling platform using MASS-GT and MATSim. An integration framework to bridge MASS-GT and MATSim was formulated in this study to model the interaction between the passenger and urban freight transport. Several scenarios are formulated to analyse the impact of crowdshipping. The results show that the CO2 and passenger-kilometres savings in parcel delivery vans transportation is exceeded heavily by the increase in passenger transportation performed by cars. This results in a slight increase in passenger-kilometres travelled and CO2 emission caused by car-based crowdshipping, although the value is very small that could be considered insignificant. It was found that car-based crowdshipping won’t either improve or worsen the impact of the current last-mile delivery system. It could be concluded that crowdshipping will be better performed using a more sustainable transport mode instead.

Convention centers are facing logistic challenges due to poor performance, lack of space and governmental regulations. This research is focused on finding strategies that can improve performance while maintaining or reducing space and complying to these governmental regulations. In this research three main strategies are introduced that can improve logistic performance at convention centers. These main strategies are: a truck appointment system, extended operational times and cross-docking. Besides these strategies specific designs are made for the case of the RAI Amsterdam. These designs differ in infrastructure and material handling equipment such as automated vehicles. It was found that implementation of a truck appointment system, extended operational times and cross-docking can improve the performance of convention center logistics. Further improvements can be made by adjusting the infrastructure and by the use of automated vehicles.

Planning the on-site transportation of large manufacturing sites is a complex process. Currently these plans are made based on the on-site logistics constraints rather than on KPIs. Research has been focused on either analysis of system parameters or generally on KPIs, but lacks the combination of both on real-life cases. This gap is closed by taking both system analysis and KPI development into account to develop a working planning tool that can assist planners in a real-life situation. The goal of this study is to gain insights in the on-site transportation planning of large manufacturing plants and their performance measurement. This study answers the question: How can the on-site transportation planning at a large manufacturing plant be improved, by 1.) adding company KPIs and 2.) data-driven decision support based on the parameters of the locality and its constraints? Through the application of the DMADE methodology, this research question is answered. The SCOR performance measurement framework is used to determine the KPIs of the on-site transportation plans. A planning model, classified as a Resource-Constrained Multi-Project Scheduling problem, is formulated as a Mixed-Integer Linear Program. This planning model optimizes the on-site transportation plans for the KPIs, proves the correctness of the KPIs and shows the potential performance increase of on-site transportation plans if constructed by the planning model.

The global market for personal mobility has transformed over the last decade. Traditional taxi services have to compete with the emergence of ride-hailing services such as Uber and Lyft. Rapid developments in available algorithms and real-time inter-connectivity of travellers and vehicles offer mobility-on-demand (MOD) services new possibilities to maximise the efficiency of the ride-hailing process. It is well established that rebalancing can enable the true potential of a ride-hailing fleet. In this context, rebalancing is defined as the redistribution of idle vehicles over the service area of a ride-hailing operator. In the search for the optimal rebalancing strategy, model predictive control (MPC) and approximate dynamic programming (ADP) methodologies are active fields of research. However, the computational burden of MPC limits the length of the predictive horizon in large MOD applications. This thesis proposes a novel algorithm that combines ADP and MPC. More specifically, we integrated a value function into the MPC framework as a terminal cost. We shorten the horizon of MPC and propose to use the value function as a long-term planner. For the terminal cost, we continue research into piece-wise linear value function approximation. We implement a novel multi-period approximation of the value function, where we use the maximum repositioning length as the horizon. In our case study, the value-based repositioning strategy offers comparable service quality to conventional MPC at 5.2% of the computational burden. The hybrid ADP-MPC algorithm offers flexible horizon partitions between the multi-period value function and MPC. It addresses the shortcomings of both ADP and MPC algorithms in repositioning problems. At peak performance, it offers an increase of 4.5% in service rate and a decrease of 5.2% in the average waiting time over conventional MPC, at roughly half the computational burden. Keywords: Ride-hailing, Mobility on demand, Model predictive control, Approximate Dynamic programming

To decrease Europe’s harmful emissions, the European Union aims to substantially increase its offshore wind energy capacity. To further develop offshore wind energy, investment in ever-larger construction vessels is necessary. Designing these vessels is difficult however, as this market is characterised by a large but seemingly unpredictable growth of market demand, turbine size and distance from shore. Currently no way of dealing with such market uncertainty within the ship design process was found to exist. This research aims to develop a method that is able to deal with market uncertainty in early ship design by increasing knowledge when design freedom is still high. The method uses uncertainty modelling prior to the requirement definition stage by performing global market research, and during the concept design stage by iteratively co-evolving the vessel design and business case in parallel. The method consists of three parts; simulating an expected market from data, modelling multiple vessel designs, and an uncertainty model that evaluates the performance of the vessels in the market. The case study into offshore wind foundation installation vessels showed that the method can provide valuable insight in the effect of ship design parameters like main dimensions, crane size and ship speed on the performance in an uncertain market. These results were used to create an initial design that is expected to perform well in the uncertain market. The developed method thus provides a way to deal with market uncertainty in the early ship design process.

Digital Twins are a key component of Industry 4.0. They are a digital representation of a real-world entity containing both the structure and the dynamics of its real-world counterpart. The use of Digital Twins appears to offer a powerful an compelling application for production processes. A Self-configurable and self-adjustable Digital Twin is developed for the Greenfield production process of FOCUS-ON. FOCUS-ON expects a fast growth in order demand and needs to adjust their production line accordingly. A Digital Twin is developed according to the 5C architecture. The Digital Twin proposes adjustments to the production line to keep up with the increasing order demand.

AGVs have seen an upward trend in development over the last 60 years. The technology has developed from mechanical bumpers and guided wire navigation to contactless sensors and free navigation technique in the current age. Further, the control on AGVs has moved from central control system to local intelligence which opens up various possibilities with respect to operations as well as applications. The growing trend of AGVs has been due to the sudden growth in digital technology and the ever-increasing demand to reduce human intervention in operations. This has resulted in increased research regarding the implementation of intelligent AGVs in areas of application that have not yet been explored, namely, light metal and forging industry. The major reasons for indulging in autonomous equipment are, increased productivity, reliability and safety since human involvement is either eliminated or largely reduced. However, a major share of the research about intelligent AGVs has been confined to warehouses and port logistics. Therefore, through this research, another area of application is investigated, namely, the light metal and forging industry, and more specifically, the potroom of an aluminium smelter. Hence, the objective of the research is as stated: With the introduction of intelligent AGVs, stochastic behavior needs to be addressed, that is, how would these AGVs react to disturbances created by such random human behaviour and process interference? Therefore, the research focusses on the routing problem in such situations which are dynamic in nature. The research aims to provide a planning approach in terms of dynamic AGV routing under the assistance of a sensor based system that can detect obstacles. The dynamic re-routing of AGVs is addressed using a mathematical formulation as well as a graphical representation. In order to solve the problem at hand, the graphical approach is followed and the objective has been simplified for research purposes. It is simplified as: For instance, if a certain pathway is blocked in the potroom of an aluminium smelter due to such stochastic behavior, how would the AGV find the optimal path? An algorithm is devised in order to answer the above research question and further implemented in Python. Various scenarios of stochastic disturbances is analysed and evaluated accordingly. Therefore, this research develops an algorithm and a subsequent model that is implemented using Python which is used to evaluate the routing of an AGV in the presence of stochastic behavior. It acts as a proof of concept for the problem at hand as it restricts the work to a simplified situation of a single AGV operation. Although this research uses the case of a light metal and forging industry, the same can be applied to industries with similar challenges such as, cement industry, power generation industry, aerospace, construction and so on. Cement, construction and power generation industry deal with environments similar to the light metal and forging industry. Further, in all these application areas, the use of AGVs for material handling would improve productivity and reliability, while improving safety of operations as well. This research focuses on a simplified situation, however, the basis of this work can be further extended to solve a more detailed real world scenario with a fleet of AGVs, and this can be done by the use of advanced heuristics.

Seaport operators are becoming more environmentally conscious and are looking to electrify their terminals to reduce their greenhouse gas emissions. This leads to higher energy-related costs and more congestion on the electricity grid. This thesis investigates the potential of demand response as a viable strategy to reduce energy-related costs. By modifying operational planning, energy consumption could be deferred from peak to off-peak hours, resulting in cost savings. Different potential ways within the terminal to provide demand response are identified. I propose a two-stage stochastic mixed-integer programming model to optimize operations planning, incorporating energy-related costs. Both energy demand and supply uncertainties are accounted for, exploring various scenarios for vessel arrival times and fluctuating electricity prices. The model is decomposed using a progressive hedging algorithm. Operational aspects considered in this model include vessel arrival scheduling, temperature control of refrigerated containers, allocation of handling capacity across quay cranes, yard cranes, and automated guided vehicles, as well as a charging schedule for the automated guided vehicles. A case study of the Altenwerder container terminal in Hamburg was conducted to test the model. Preliminary results suggest potential cost savings in the range of 12.0-13.2 % with a varying electricity prices based on wholesale market rates. Furthermore, it was found that stochastic modeling improved the solutions found of up to 20.6 % compared to a deterministic model. These findings underscore the substantial potential of demand response strategies in the context of container terminal operations

Collaborative Port Call Optimization (PCO) is a key strategy to improve port efficiency. This study aims to bridge existing research gaps by providing a holistic understanding of collaborative PCO adoption, emphasizing the complexities of multi-organization collaboration and the influence of emerging technologies within the nautical chain and Port Governance structures. The TOEI framework, a novel conceptual model that extends the Technological Organizational-Environmental (TOE) framework, offers a valuable tool for port authorities and operators, providing specific insights and recommendations to enhance port call processes collaboratively. The study targets not only academics but also policymakers, port authorities, and nautical service providers. The findings discovered through the empirical analysis and the development of the TOE framework provide valuable insights for ports seeking to enhance their port calls collaboratively, laying the groundwork for future research, encouraging further exploration of specific contextual factors, in-depth case studies, and the development of tailored strategies for diverse ports globally.

This paper describes a real-time energy management system developed for a solar park in the Netherlands using an alkaline electrolyser. The optimization problem is split into a two-step optimization, taking into account the specifications of the electrolyser and allowing the electrolyser to respond to changes in the imbalance market. The first optimization step determines the state of the electrolyser one day in advance. The second optimization step determines the electrolyser power, using the state of the electrolyser as an input. Simulations using data from 2020, 2021 and 2022 show that the use of the electrolyser is limited to a number of days in the year with a lot of solar generation, causing the day-ahead prices to be low. Different scenarios have been tested to get insight into how the use of the electrolyser is influenced by these changes. The type of electrolyser, being able to put the electrolyser on standby and allowing the grid to be used for the electrolyser hardly affected the results. At last, different hydrogen prices are compared. The higher hydrogen prices lead to more use of the electrolyser. This real-time EMS contributes to the ability of an alkaline electrolyser to respond to the sudden changes in the grid and by doing this making it possible to use alkaline electrolysers for balancing the grid and contributes to the use of green hydrogen in the industry for a competitive price.

The establishment of areas for safe automated driving in mixed-traffic settings is one major barrier in the development and adoption of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users such as cyclists and pedestrians in a simulated urban environment in the Dutch city of Rotterdam. Proposing new junction and pedestrian models, virtual AVs with an occlusion aware driving system are deployed to deliver cargo autonomously. Assessing the impact of various measures, including V2V, V2I, V2X communications, infrastructure modifications, and driving behavior, we show that traffic safety and network efficiency can be achieved in a living lab setting for the considered case. Our findings further suggest that V2X gets implemented, new buildings are not placed close to intersections, and the speed limit of non-arterial roads is lowered.

E-bike sharing has gradually gained popularity in recent years, while the research in this field is still quite limited. This study applies data-driven methods, mainly demand pattern analysis, to facilitate the development of operational strategies in a cost-effective and operator-friendly way. Demand pattern is analysed in an innovative spatial analytical unit, overlapping circle, which is proven to achieve more beneficial effects than the traditional units (i.e., the administrative units), and hourly clustering is conducted to derive the reallocation strategies by mitigations of imbalance in supply and demand in recurrent hourly clusters. Additionally, this work constructs several indicators to evaluate the strategies in a real-life context, taking both the operator and the users into account. The proposed methodology is applied in a case study, bondi’s e-bike sharing in The Hague with a 4-month time frame from 19-06-2021. There are 5 hourly clusters emerging via agglomerative hierarchical clustering: 1) the first peak hour (16:00-16:59); 2) the second peak hour (17:00-17:59); 3) the first transition hour (18:00-18:59); 4) the second transition hour (19:00-19:59); 5) the off peak (20:00-15:59). The corresponding reallocation strategies are then proposed to alleviate the imbalance in different periods. Additionally, adjustment in the operational areas is suggested by the supply efficiency and trip duration/distance analyses. The results prove that the operational strategies obtained from demand patterns indeed improve the service, with almost 1.5 times ridership, circa 20% decrease in vehicle idle time, compared to the baseline. and a decent monthly net retention rate at around 60%.

Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based round-trip bikesharing (SBRT) scheme located at train stations throughout the country. This scheme allows users to rent a bike to connect the train station and their destination. The round-trip characteristic SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. While on the latter a wide range of research exists, little research has been conducted on round-trip bikesharing, especially when being integrated into an existing public transport scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system OV-fiets using multiple linear regression (MLR). The results are compared with findings on one-way bikesharing schemes. The results are then used as an input to forecast short-term demand. To identify a best performing forecasting method, the statistical methods MLR and Prophet are compared with the neural-network based method Long Short-Term Memory (LSTM). It is found that for hourly rentals in an SBRT-system, the highest explanatory power achieved with the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Further, the magnitude of the correlation between the determinants and the hourly demand differs across the stations in the system. For forecasting, the performance of the methods differs across the stations and forecasted periods due to the stations' distinct characteristics. But, especially in times of uncertainty, LSTM is likely to outperform the others due to it's capability of adapting to short-term changes in the demand.

This paper focuses on the effect of different storage policies on the performance of RoboticMobile Fulfilment Systems (RMFSs). The research is conducted under the instructions of the Technical University of Delft and CEVA Logistics the Hague. The aim of the research is to develop a decision support tool that can aid companies in the choice of the storage policy to use for their RMFS. RMFSs have multiple different levels of decision problems that need to be solved. The performance of a RMFS highly depends on the algorithms that are applied to solve these decision problems. This research focuses only on the storage policies of a RMFS. In this case storage policy refers to the decision in which pod items should be stored and where on the storage area the pod should be positioned. In order to gain a good understanding of the effect of such a policy on the overall performance of a RMFS, experiments should be performed. Physical experiments are however very hard and costly to perform. This research therefore makes use of a simulation study to test different storage policies in different scenarios. The simulation model used is an adaptation on an agent-based semi-open queuing network framework model by Merschformann et al. (2018a). In the experiments four different storage policies are investigated under three different storage layouts. The results of the simulations are analysed via the throughput, the pile-on and the distance travelled during the simulation. After this a score is given to both the picking as well as the replenishment side of the system. It is important to investigate both sides of the system since the flaws on one side can negatively affect the other side. The scores of the replenishment and picking processes are then averaged to gain a final score which indicates the overall performance of the policies. Finally a fifth policy has been developed where each pod can contain multiple different sized compartments. Unfortunately testing and verification of this policy was not possible in the given time frame. For this reason it has been left out of the experiments.

In recent years blockchain technology has become a popular topic for research in multiple fields of discipline. For the transport sector especially, the technology is seen as disruptive. Because the research field is young and the research on the topic is developing fast, state-of-the-art knowledge is exclusive to a select group of academics. This review aims to provide an overview of the current state of blockchain technology applications in the transportation domain. Previous reviews on blockchain and transportation have already mapped the contributions for supply chain and logistics. Studies within the transportation domain on connected systems and vehicles have not been comprehensively reviewed, while the relevance on these kind of systems is growing. The focus of this review is on Intelligent Transportation Systems (ITS), which integrates the connected transportation systems and vehicles. The demand for ITS is growing and the relevance of supporting blockchain solutions as well. The increasing amount of connected vehicles introduce security and privacy concerns. Connected vehicles share sensitive information on which other users base their behaviour, so correctness of information is very important. Next to that, the risk arises of losing complete control over the vehicle through attacks due to the remote accessibility possibilities of the vehicle. Blockchain offers a solution for these concerns by enabling a decentralized and secure way of sharing information. These features make secure and trustworthy communication between vehicles, roadside units and platoons possible, aswell as secure over-the-air software updates. Privacy concerns can be mitigated via the use of pseudonyms, partitions or fresh data keys. With its decentralized characteristic, blockchain technology creates a transparent and traceable way of storing and sharing data. For ITS applications this means efficient insurance processes and liability attribution. Furthermore, the traceability feature of blockchain can be used in fraud detection for rolling wrecks and odometer fraud. Also, ITS can benefit from blockchain technology by integrating smart contracts. Processes where a third party is involved can be replaced by a smart contract, making it possible to implement vehicle-to-machine payments. This makes transactions fast, cheaper and secure. Processes like tolling payments or vehicle maintenance can then be automated. The security and privacy features of blockchain technology offer a solution for the implementation of Mobility-as-a-Service principles, which enables vehicle sharing. Furthermore, blockchain is an asset for transportation solutions in smart cities. In smart cities, blockchain technology can ensure data integrity and encourage organizations to share data enabling transparent city management. Within smart cities, blockchain can facilitate the data sharing between vehicles and infrastructure, as well as energy trading via vehicle-to-grid principles. Another way in which ITS can benefit from blockchain technology, is through the use of incentive mechanisms. Blockchain is well suited for incentive systems because it can automate direct interaction between actors. With automated payments, people can be incentivized to share reliable traffic data or to participate in efficient energy distribution for the grid. Also, safe driving behaviour and environmental friendly transportation can be rewarded. Before real life systems can benefit from the advantages of blockchain technology a lot of challenges have to be overcome. For ITS, all applications exist only in theoretical form. Challenges that have to be faced are the high energy consumption, latency and throughput constraints, and governance issues. Although the benefits of blockchain technology are multifold, the challenges have proven to be to hard to overcome up until now.