| dc.description.abstract | The inducement of this study is the limited knowledge of agricultural traffic intensity on the Dutch road. The expectation is that several developments result in an increase of this intensity, for example the ongoing growth of farm sizes. An agricultural traffic increase may result in conflicts with other road users. It is difficult to automatically count agricultural vehicles with infrastructural based devices. Furthermore agricultural traffic intensity fluctuates, because of the seasons. Consequently it is not possible to interpolate measured
locations to estimate unmeasured locations, a methodology used in several car traffic density studies. Hence the objective is to model
the agricultural traffic intensity in space, and time. Therefore the Kadaster model is extended. This model calculates the routes between a home and field parcel, but does not show how frequent this route is used. Therefore has been researched in this study which variables of the start, and endpoint influence the number of times a route is used by a farmer which is expressed in rides. A ride is trip from the home to the field parcel, and back. The five research questions of this study examine: the factors influencing the number of rides, implementing these factors, analysing the implementation, examining the influence of time, and validating the results. A network-based Geographic Information System is used as a method to calculate the routes, and rides. The researched factors that influence the number of rides are; crop type, soil type, and dumper size. Expert interviews are done in the Noordoostpolder, to research the implementation, and the validation. The interviews showed that the factor quantifications gave a first
good view, but that some quantifications are adapted as a result of agricultural developments. The study result is a model that estimates the agricultural traffic intensity on road segments, for different time frames, and can give an impression of the traffic intensity pattern in a region. Because several uses of agricultural vehicles are not taken intoaccount, it is expected that the actual intensity is underestimated. The model results are compared with a study by the province of Zeeland, to the number of agricultural vehicles on an average workday. This comparison showed a weak correlation. It was difficult to compare both studies, because the methodologies were different. It is therefore recommended to research further validation options, and to extend other uses of agricultural vehicles. In the future it may be possible to use GPS tracking systems of agricultural vehicles to follow their movements. For now these systems on the researched agricultural vehicles are not yet commonplace and not suited for on-road tracking. | |
