Route planning is an important part for companies that transport goods between different locations. To optimize the route planning, it is important that the travel time predictions come close to reality. A too tight schedule would lead to pressure to the driver, late fees and reputation damage, but a too loose schedule would result in wasted capacity. To predict the travel time, first a speed has to be assigned to each road in the digital map, after which the shortest path can be calculated. This research proposes a speed prediction model based on neural networks to predict the speed of each road such that the travel time prediction accuracy is improved. The neural network speed prediction model will be compared to the current speed prediction model, which is based on random forest. The speed prediction model first learns from preprocessed GPS data that is obtained from two different companies that operate with trucks. After relationships have been found between the driven speed and road properties (e.g. speed limit, road width, tunnel, etc.), the speed prediction model predicts a speed for each road in the digital map. Subsequently, the shortest path can be calculated from the first to the last GPS point of different routes. Then the predicted travel times from the shortest paths were compared to the real driven travel times. After comparison, it turned out that for both GPS data sets, the neural network speed prediction model did not outperform the travel time prediction accuracy in sMdAPE_TT of the random forest speed prediction model.

This report is the fourth and final report concluding the spring Design Synthesis Exercise for group 07. Ten bachelor students from the faculty of Aerospace Engineering at Delft University of Technology have worked eleven weeks on a given assignment: the design of an executive jet with amphibious capabilities. This report is aimed at peers, meaning basic engineering knowledge at the level of a third year bachelor student is assumed of the reader throughout the report...