This study researches how station-based bike-sharing can be implemented in strategic transport models. Implementing station-based bike-sharing is a challenging topic, as it requires to combine two modelling techniques. The first is modelling transport tours to make sure that a person who rents a bike returns the bike to the same station. The second challenge is to model multimodality, because shared-bike are always used in combination with other modes. The most prevalent approach on the market to combine the topics is the two-step mode-choice approach. However, the two-step mode choice approach has three important limitations to model SB-bike-sharing. Therefore a new approach is proposed in this study; “The tour-based mode-chain and station choice approach”. Based on a small-scale theoretical case-study the new method gives promising results regarding modelling the combination of tour-based-mode-choice and multimodality.

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.

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.

Dynamic traffic management (DTM) plays an important role from Dutch policy perspective to prevent road congestion and has been developed from control strategies to services. Five traffic control centers, 22 different DTM systems with 35 functions and over 50,000 DTM components make up the national traffic management network in the Netherlands. The malfunctioning of the DTM systems is expected to create negative impacts to the traffic, proper maintenance planning is necessary to ensure their availabilities. However, there is less knowledge about the DTM malfunctions, which makes it difficult to monetize the malfunction effects and therefore to optimally deploy the maintenance budget. In this research, a macroscopic dynamic traffic assignment model “MARPLE” is used to evaluate the social costs of the DTM malfunctions according to the failure function, failure duration, and failure location. The motorway network around Amsterdam is chosen as the study area in this research, and four DTM systems and measures were evaluated, including the rush hour lane (RHL), the motorway traffic management (MTM) system, the dynamic route information panels (DRIPs) and the ramp metering (RM) system. By conversing the DTM malfunctions into the motorway network, the introduced impacts to the traffic both in local and network levels are identified. This research made the first attempt to modify DTM malfunctions in a macroscopic dynamic traffic assignment model, and a methodology was developed to calculate the malfunction costs both in traffic flow and safety aspects. The outcome of this research answered what-if questions with regarding to DTM malfunctions, it also proved the feasibility of the ambition to translate the DTM malfunction impacts at a network level into its social costs, according to which the maintenance strategy for the DTM systems can be better deployed. Overall, the initial goal of calculating the malfunction costs of the DTM systems with a newly developed methodology is met. Through the identified limitations and improvement strategies, the framework developed in this study could offer the possibility to refine the analysis, and/or easily be applied to other DTM systems and road parts.

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.

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)

In this thesis an investigation is performed into the effect of different speed limits on freeway capacity. From literature, much is known about the variety of factors that affect capacity, but the exact effect of the speed limit on capacity is not yet clear. In recent years, several speed limits changes have taken place at multiple two-lane freeway bottlenecks throughout The Netherlands, which makes it possible to compare effects of different speed limits at the same location. To evaluate the effect of the speed limit on capacity, the Product Limit Method has been applied to identify breakdown flows and generate capacity distributions, which could subsequently be compared for different limits. In the comparison of capacity distributions under different speed limits, it was found that significant changes in capacity had occurred, but that no uniform direction of the effect could be found. Subsequently, to control for location specific factors and other variables, Fixed Effects regression has been used to determine the effect of the speed limit on the breakdown flow. It was found that the breakdown flow under the 120 km/h limit was significantly higher than under the 130 km/h limit (in the range of 60 to 190 vehicles per hour) and that the breakdown flow under the 100 km/h limit was, in some cases, also higher than under the 130 km/h limit. In addition to this, it was found that a significant positive relation exists between the height of the speed limit and the fraction of flow in the passing lane. Moreover, it was shown that the relation between the fraction of flow in the passing lane and the level of breakdown flow was best represented by a quadratic relation, which could indicate that an “optimal” distribution of flows may exist. Given the results of this thesis, it is posed that a change in the speed limit is likely to affect capacity primarily through altering the lane flow distribution and that it will depend on the layout of a freeway location what the optimal lane flow distribution is and which speed limit leads to this optimal lane flow distribution.

The past decade has seen a rise in ability to collect, store and manage large amounts of data. With data becoming increasingly available to businesses, organizations are looking to new ways to gain insight into their activities. Acquired information can help transportation companies to cope with industry trends of expanded competition, the increasing pressure for lower transportation costs and higher service levels. The challenge for truckload carrier companies lies in determining which data is of value to the business. Understanding performance indicators and how to measure these is an important aspect of using data. To propose a solution, a framework is developed, in which clear performance indicators are reported, as well as how these can be measured.