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.