Installing wireless sensors on a network of waste containers, that monitor at regular intervals their waste fill levels and transmit the data to the cloud of a waste management operator over the internet, describes the Internet of Things (IoT). With this constant stream of data, the dynamic organization of waste collection schedules is enabled as containers are visited for collection only when it is necessary, which consequently leads to demand-responsive services. Domain experts attest that operating a demand-responsive service brings financial and environmental gains to a waste collection service, but it simultaneously introduces complete variability in the system which is undesirable in real-life operations. To solve this problem and consequently optimize the waste collection service’s performance, domain experts stress the need for a balanced trade-off between dispatch consistency and flexibility. This means, being able to exploit to the highest degree possible the benefits of demand-responsive operations, while also maintaining a certain level of dispatch consistency when demand varies from day to day. This research focused on developing a solution approach to solve the IoT-based waste collection problem, which is derived from the knowledge and requirements of the domain. The use of the knowledge of the domain is significant as it ensures that the model is tailored and applicable to a real-life IoT-based waste collection service. The overarching objective of the approach is to maintain dispatch consistency and flexibility when the containers’ location and demands vary from day to day, as well as to attain an economically and environmentally enhanced waste collection performance.

In recent years, the amount of traffic on the highways has increased continuously and in particular freight traffic. Despite solutions to maximize roadway capacity, the drop of capacity after congestions sets in, remains an active field of study. The influence of freight traffic on traffic flow has received little attention, especially regarding the capacity drop. The Kaplan-Meier Product Limit Method was used to estimate capacity and recovery distributions for selected sites to overcome stochastic characteristics of traffic flow and investigate the relation between heavy vehicle share and capacity drop at Dutch highways. Furthermore, a simulation study was executed to investigate increased heavy vehicle share scenarios and changing physical and operational characteristics of heavy vehicles. The empirical results show a connection between heavy vehicle share and the capacity drop, although not statistically significant. Currently, chaotic properties of breakdown flow seem to superimpose the impact. However, it is possible that the effect becomes influential as the current maximum observed share of heavy vehicles during breakdown grows from 9% up to 15%, which appeared to be the worst case scenario in simulation. Besides, concern is raised as a decreasing breakdown capacity was observed on several Dutch highways, even after correction with the PCE values of the increasing heavy vehicle share.

The synergic effect between the spatial organisation and freight transport is crucial for transport planners to understand. It will help in improving freight transport planning. The absence of freight data has made such a study difficult. This research aims to understand the relation between spatial organisation and freight transportation by using the 1681 logistics facility database and the database of 2.65 million shipments of the Netherlands collected by CBS in 2015. The logistics facilities are categorised based on different spatial and logistics characteristics. The indicators of freight activity for the logistics facilities are studied, and the transport efficiencies are analysed subsequently. The analysis concluded on three main points. First, there are eight main types of logistics facilities in terms of spatial characteristics. Second, spatial characteristics have an impact on freight activity. Finally, the transport efficiency of the different logistics facilities can not be directly compared due to their objective and position in the supply chain.

The agent - based modeling of freight transport that captures both the variety of actors and their complex decisions has not made as much progress as in passenger transport. Efforts towards agent-based models must include firms. However, the synthesis of a firm population in the context of urban freight transport has gotten little attention in literature. This research focuses on synthesis of a firm population for urban freight transport demand model. It uses literature review to define the firm agents and specifies a general population synthesis model based on iterative proportional updating (IPU). The model is then implemented on a case study on the province of South Holland. The utilized data is open, free-of-charge data offered by the Central Bureau for Statistics (CBS) of The Netherlands. The synthesis uses a sample-free, indicator-based approach to initialize the IPU-based model. The results show correlation between firms’ distribution and urban density of neighborhoods for most of the population. However, the suitability of a neighborhood to host a firm type has a more nuanced definition that cannot be encapsulated in a single indicator. More robust, economic-sector specific indicators are needed to disaggregate firm agents to neighborhoods and finer levels. Further research into IPU-based, sample-free firm synthesis models is recommended.

Urban freight transport is a significant cause of CO2 emissions. This creates the need to implement CO2 mitigation measures for freight transport in cities. The International Transport Forum (ITF) is currently working on an EU-funded programme, the Decarbonising Transport initiative, to assess CO2 mitigation measures. However, there is no model yet that is suitable to analyse urban freight transport on a European level. Therefore, the research question of this thesis is: How can a model for urban freight transport in the urban region of Rotterdam (Groot-Rijnmond) be created, that allows for estimation of CO2 impacts of policy measures for decarbonisation and that can be transferred to other European urban areas? To answer this question, a literature study has been done to set the context of the project. This was followed by an identification of available data sources for European freight transport and the creation of a transferable modelling methodology. Based on the data and the established methodology, a model has been estimated. With this model, seven policy scenarios have been tested.