Urban areas with many daily commuters often experience a reduction in traffic flow during rush hours. In some urban areas where this reduction in traffic flow occurs, there are popular destinations for leisure that have multiple large parking facilities. Vehicles searching for vacant parking spots drive at a lower vehicle speed, reducing the traffic flow even further. Traffic flow can be improved by using a traffic signal control strategy and a smart parking solution to increase the speed of vehicles searching for vacant parking spots. This thesis combines the use of Model Predictive Control (MPC) in traffic signal control, with a Parking Resource Allocation Model (PRAM) to further improve the traffic flow on the traffic network. The PRAM considers the travel time of all travel routes to the parking areas, as well as the distribution of vehicles toward the parking areas.
The performance of the combined MPC and PRAM control strategy is compared using two case studies. In both case studies, the traffic network is a simplified representation of the mall of the Netherlands, located near Leidschendam. The first case study simulates traffic based on the morning rush hours, and the second case study simulates traffic based on the evening rush hours. In both case studies, multiple traffic demands are simulated, based on historical traffic data provided by Rijkswaterstaat. No historical data is provided by the mall of the Netherlands so fictive data is used. The performance of an MPC control strategy, an MPC control strategy with the first PRAM, and an MPC control strategy with the second PRAM is compared with a fixed-time control strategy for these traffic demands. The results show that the MPC control strategy reduces the total time spent and vehicle time loss of all the vehicles for the morning rush hours, but not for the evening rush hours. For one traffic demand, the added PRAMs further reduce the total time spent and vehicle time loss of all the vehicles, in both case studies. For the other traffic demands, the added PRAMs increased the total time spent and vehicle time loss. There is no significant difference between the two PRAMs in terms of the total time spent and vehicle time loss. Furthermore, the distribution of vehicles to the parking areas is more evenly for the second PRAM. Since the parking demand is fictive, future research is necessary with accurate parking demand to ensure that the combined MPC and PRAM further improves the traffic flow on the traffic network of the mall of the Netherlands. Moreover, future research is needed to more accurately predict the travel time of travel routes.

When traffic demand is high and the intersection lacks throughput, oversaturated traffic phenomena occur, such as the oversaturation of a turning bay or intersection link. When such unsafe situations emerge, control strategies can be applied to handle or avoid these spillback effects. In this paper, a spillback component is designed that detects and controls spillback in a real-time controlled traffic system (RTCS) that optimizes multiple vehicle types in its optimization, with a high vehicle priority for the light-rail vehicle (LRV). A spillback control strategy was developed that assigns additional priority to the congested link when spillback is detected. For assessment, a simulation study is conducted to gain insight into the effects of applying spillback control strategies on the traffic and network performance at an isolated intersection and in a corridor. The results showed that the intersection performance decreased when a spillback control strategy was applied in the optimization, but the network performance increased during the peak period. Furthermore, a spillback penalty factor effectively decreases spillback durations while it works well in combination with higher prioritized public transit. A too-high priority negatively impacts the intersection and network performance. However, in some cases, a (high) spillback priority is necessary to provide enough priority so that a growing unsafe situation is avoided. Therefore, it is crucial to determine the intended use and applicability when providing spillback priority. The spillback priority is one of the few priorities that can be configured in the utilized RTCS; it is recommended to research the impacts when varying priorities are used.

The outbreak of the COVID-19 pandemic and the resulting corona crisis has had a profound impact on mobility in the Netherlands and in the entire world. This study aims at predicting, modeling, and analyzing changes in the activity-travel behavior of individuals in the Rotterdam-The Hague Metropolitan Area, the Netherlands, during different stages of the COVID-19 pandemic and assess their effects on mobility. This is done by using the activity-based travel modeling approach. A modeling framework is developed and used for this study. The modeling framework provides an innovative approach to study the impacts of changes in activity-travel behavior caused by emergency situations such as the COVID-19 pandemic in a disaggregated manner. It combines the outputs of ABMs and a mix of aggregated and disaggregated data of changes in in-home and out-home activity frequencies and re-estimates the daily schedule of individuals considering factors and attributes that were not considered before, such as the weekly frequency at which agents do different activities and the sector where agents work. With the outcomes of the case study simulations, insights about the effects on mobility were identified and compared to real data counts and results of other studies. The conclusion is that the vast majority of the model outcomes proved to be in line with the results of other sources, and the modeling framework can be a useful tool to study the changes in activity-travel behavior of individuals during emergency situations and their effects on mobility.

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.

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.

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.

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 European Union Road freight transport volume is expected to grow 78% between 2000 and 2030, which results in more trucks on the road network. The worldwide estimated trend shows an increase of 150 million freight vehicles and an increase of 240 million passenger vehicles. The growth of both vehicle classes will have a major impact on the road network and the roads will become congested. Especially in dense urban environments with many intersections. Further, trucks have a detrimental impact on traffic flows, especially at intersections, because of their slow dynamics and large size. In addition, a stopping truck results in higher emissions and fuel consumption compared to a car. However, today’s traffic controllers are not capable of optimizing traffic flow at intersections based on classification of different vehicles. Therefore, it would be beneficial to all vehicles involved if the number of stops for trucks would be reduced to a minimum, by servicing each vehicle class in a different way.
Throughout this research the focus is to develop a signalized intersection controller which is able to reduce the number of stops for trucks, while maintaining an equal level of service for the other modes of transport. Extensive literature studies provided important insights into the development of signalized intersection controllers. A selection of the techniques found in the literature is used to develop a new truck signal priority controller design. The basic idea is to use a weighted traffic light schedule in combination with priority weights to enable truck signal priority. The design is evaluated in a case study and simulated for multiple configurations. This leads to an overview of the performance in terms of the number of stops and vehicle delay.
From the results several conclusions can be drawn on the optimal weight configuration, which is compared to a state-of-the-art model predictive intersection controller. The results showed an reduction of the total number of stops by 751 and total vehicle delay by 304 minutes for trucks over a week (26,6% and 20,4% respectively). While, the total number of stops and total vehicle delay for cars increased, by 155 stops and 201 minutes (0,1% and 0,3% respectively). However, the overall total number of stops and total vehicle delay were reduced by 596 stops and 103 minutes (0,42% and 0,16% respectively). It can be concluded that the proposed truck signal priority controller design can reduce the number of stops for trucks at a signalized intersection, while maintaining the overall traffic flow at least as good as a state-of-the-art model predictive intersection controller.