The growing demand for parcel deliveries is causing significant problems for freight logistics, particularly in cities. The increasing number of delivery vehicles leads to traffic congestion, high emissions, and rising costs. To address these problems, new and sustainable delivery methods must be implemented for the parcel last-mile. However, estimating the impact of a different logistics system is complex, as it depends on consumer adoption of these new delivery methods. This paper presents a new simulation model that captures and explores the interconnections between multiple delivery methods and consumer preferences for those delivery methods. Consumers’ reaction to the performance and availability of the delivery methods is simulated, next to knowledge progress via word of mouth and familiarisation. The developed hybrid model uses system dynamics to simulate the evolution of consumer preferences and the last-mile delivery via van, self-collection, crowdshipping and drone delivery at an aggregate level, while an agent-based model provides input on the operational performance of the delivery methods. With this structure, consecutive interactions can be simulated, and by that, data on consumer preferences and the delivery method operations can be obtained at multiple time points. Results of a case study on the province of South Holland show that consumers change their preferences due to the introduction of new delivery methods. However, the vehicle kilometres and emissions of van delivery do not reduce at the same rate as the demand. The study also highlights zonal differences in last-mile services and the importance of delivery quality to consumers.

E-commerce has grown rapidly worldwide over the past decade and thus more parcels are shipped into urban areas. The last mile delivery of these parcels into an urban area create big problems such as congestion and pollution. In order to tackle these problems new solutions have been found by either changing the operations of the system or using different vehicles. It is difficult to predict what will happen when these solutions are implemented in an urban area. Thus freight models are required to evaluate these last-mile solutions. This research used Agent-based modeling (ABM) to evaluate last-mile solutions for the delivery of parcels in a zero-emission urban area. The four last-mile solutions that were evaluated are vehicle type, collaboration levels, varying number of UCCs and varying parcel acceptance rates. The results show that electric delivery vans perform better than cargo bikes based on distance traveled, cost and number of vehicles required. On collaboration levels it shows that full collaboration can have a positive impact on distance travel traveled and cost. It is suggested to use two UCCs instead of five since having more UCCs does not decrease distance traveled and costs. The results also showed that when all parcels are accepted in the system on their first try it reduces cost, distance traveled by all vehicles and the total number of vehicles required. Local administrators can use these results when tackling the impact ever increasing growth of e-commerce and the parcels that need to be delivered.

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.

This research clarifies the relations between the growing e-commerce sector and the logistic sector. The last-mile transportation of goods bought via e-commerce is growing, which leads to negative impacts on the environment and mobility. A Causal Loop Diagram (CLD) illustrating the relations is made and a simulation model is build quantifying these relations, for both models System Dynamics (SD) is used. The models are based on literature and data from CBS. Strategies are developed to assess two policy strategies, the use of (unmanned) pick-up points and the use of light electric freight vehicles (LEFV). The growth of e-commerce leads to logistic efficiency at the beginning, but eventually this efficiency growth will stop. Currently their is still logistic efficiency gained, this results in a less steeper growth of the negative impacts. The use of pick-up points is based on the same parameters providing the logistic efficiencies and are a strong long term solution to the growth. Furthermore, using pick-up points increases the rate of successful first time delivery, this results in less needed shipping capacity. Passenger transport is needed to pick-up the package, this effect is not incorporated. The use of LEFV’s reduces the amount of emitted pollution and the amount of road traffic movements. However more vehicles are needed and the traffic movements will only be moved to bicycle lanes. Both strategies are realistic and some logistic service providers are already implementing the strategies.

The increasing parcel demand is resulting in increasing traffic congestion and emission problems in urban freight system. Crowdshipping service is an innovative logistics solution which envisions using the excess capacity in existing passenger transport to perform the delivery tasks. However, it may bring extra vehicle kilometres travelled due to detours and new trips generated. To address this challenge, most studies focus on designing and evaluating the crowdshipping service for all parcel demand, while one study points out the potentially larger benefits of crowdshipping service for outlier parcels. Therefore, this study aims to investigate the transport impacts of crowdshipping service for outlier parcels, which are defined as the parcels with high environmental impacts. A case study is conducted in The Hague. First, the parcel carbon footprint is calculated to segregate the outlier parcels. Then, a public transport-based crowdshipping delivery scenario is proposed, with parcel lockers at train stations as the transfer points and train travellers as the potential occasional couriers. The simulation results show that outsourcing the outlier parcels to crowdshipping service is beneficial to the transport system and prioritising outlier parcels of logistics service providers with low market shares can achieve more savings in transport and higher service efficiency.

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.

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.

Tour formation is a distinguishing feature of freight transportation. An increasing amount of research is dedicated to the consideration of tour formation in freight simulation models, but these tour formation models lack statistical calibration on empirical data, do not represent shipments explicitly, or focus on a narrow segment of freight transportation. This paper presents a novel shipment‐based approach to model tour formation behavior. We developed an algorithm that constructs tours through iterative allocation of an additional shipment. Two choice models provide an empirical foundation to this algorithm. Parameters of these choice models are estimated on a dataset with information regarding over two million shipments. This information is gathered automatically from the planning systems of carriers transporting goods in the Netherlands. With this model we are able to reproduce observed tour patterns excellently for a given set of shipments. In addition, the model considers many objectives and constraints that determine the tour formation process and acknowledges differences between goods, vehicle, and location types. For example, shipments at ports tend to be transported with direct tours, while tours starting at a distribution center have more stops. This model can be applied in a shipment‐based freight simulation framework to construct tours for large third‐party carriers in the Netherlands.

In recent decades, logistic markets havebeen changing. Ecommerce is growing steadily, and the recent Covid19 pandemicgave an extra boost to that. Besides, customers are seeking more flexibility inthe logistic services. Parcel delivery is changing, yet this leads to variousnegative externalities including congestion, air, and noise pollution. Theseeffects let companies seek innovative solutions for their parcel transport. Oneof these innovations is ’crowdshipping’,parcel delivery done by the crowd instead of conventional delivery companies.By making use of existing passenger transport rather than a speciallydispatched driver to ship parcels, parcel shipping should be economically and environmentallymore sustainable. Crowdshipping is a service that has shown potential in pilotsand smallscale researches. However, strategic analyses of the impact ofcrowdshipping on all actors in the transport systems are still lacking. Thegoal of this research is to explore the interactions between travellers and parcelshipments in various strategic crowdshipping contexts and assess their impacton transport use. To achieve that, the following main research question will beanswered: ’How could sustainable crowdshipping impact freightand passenger transport use?’. A simulation model is built using agentbased modelling to explorebehaviour and simulate possible effects. This model consists of interactionsbetween four agents in the system; the customers,crowdshipping platform, travellers and occasional carriers. First, thecustomers place their orders at the platform. When travellers make their trip,they could consider carrying a parcel along their way. They notice their plannedtrip to the platform, which will calculate the optimal parcels for them. Thetravellers could opt for one of the parcels and turn into occasional carriers.The impact of crowdshipping is assessed by calculating the detour theoccasional carriers travel to deliver their parcels. Other outcomes are the providedcompensation and percentage of matched parcels, to determine the viability ofthe platform. The spatial demarcation of the simulation is most of the provinceof South Holland in the Netherlands. In this study area, 2.3 million peoplereside who order over 220,000 parcels each day. Furthermore, 4.1 million tripsare made daily by car and bicycle. Taking the willingness of both customers andtravellers into account, 13,000 parcels and 750,000 traveller trips enter themodel. Four experiments are performed to inspect system behaviour in variouscontexts. The results show that implementing crowdshipping in this study areacould be viable. The average provided compensation is lower than the priceconsignors currently pay for conventional delivery. Besides, the delivery degreeseems acceptable to get a decent level of service. Through crowdshipping, thetravelled distance in the passenger transport system will increase because ofthe detours taken by occasional carriers. This increase subsequently leads to adecrease in freight transport distance through a decreased demand inconventional parcel demand. However, the passenger transport increase exceeds thefreight transport decrease. The crowdshipping platform could limit the takendetours by making strategic choices in their implementation. This might be atthe expense of their delivery degree. It is advised for the public authority toset boundaries for the platform and stimulate strategic matching choices basedon these possible externalities. When interpreting these results, cautionshould be taken. The approach has some shortcomings regarding the spatialdistribution of detours, costs of platform’s viabilityand first leg distances for parcel transport. Furthermore, limitations could befound in the assumptions made for the simulation model. This includes theabstraction that travellers do not deviate from their planned trips andmodalities, and travellers could only carry one parcel. Another simplificationis made in the matching strategy by the platform which might have led tosuboptimal drivers for the parcels. Other limitations are caused by flawed datause. Travellers’ and customers’willingness could therefore be unreliable. Also, pedestrian and publictransport travellers are not considered due to data deficiency. Furtherresearch could be done in three ways within this field of study. First, moredata can be gathered to solve the abovementioned limitations. This includesdata on preferential routes for occasional carriers and willingness data fortravellers in all modes. Secondly, other conceptual choices can be made tooptimise the detours per parcel with forecasted travellers, or to conceptualisethe collaboration between conventional and crowdshipping delivery. Finally,research can be done to study intervention methods and corresponding legalpossibilities for the public administration to limit travellers’ detours.

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.