# Using Neural Networks for Bicycle Route Planning
## 5 Discussion  
This paper wanted to test the use of the NNs for bicycle route planning because it would allow the transfer of human expertise in bicycle route selection from already established routes to new ones. The two models were trained: one on the data containing Knin roads; another on the data containing Vrgorac roads. We evaluated the NN performance by creating the bicycle paths in the Knin area using the Vrgorac model and vice versa.  
The evaluation showed a high percentage overlapping between the NN created routes and the human-made bicycle routes in the target area. In some scenarios, the NN produced exactly the same result as the human experts, and the worst result was 75% overlap.  
From this, we concluded that NN performs well and that the trained models are not overfitted. Furthermore, using the adaption learning function in the NN training process also included a bias learning function, which prevented overfitting.  
We evaluated the NN performance using MSE calculation; it was used for all models, and the steep Vrgorac model had the biggest MSE of 0.0809. However, even though this calculation indicated our model's quality, since the models are used on roads in different areas, we think that the final quality indicator was a cross-validation check that was previously mentioned.  
One of the primary inputs to the NN was the TIN relief model. It was reconstructed using layers with a height data resolution of 50 m. Thus, the resolution of the elevation model depended on how the steep ground was, meaning that the higher resolution was in places where the surface is more variable.  
When calculating the distance between each segment and the emergency unit and water source, it was decided to consider the segment as a point located at the centre of the segment. This was a compromise between the precision and calculation time. It primarily affected the calculation of the distance to a water source. The water source we used was a polyline, and it was needed to find the nearest water source segment to each road segment. Using all points the polyline consists of to present the segment would make the calculation very time-consuming. In our model, the mean length of the segment was 1556.06 m, while the mean distance to the emergency unit and water source is 22,388.26 m.  
Besides the experiment in which we checked the ability of two NN models trained on different areas to produce good bicycle paths, we compared the paths created by those two models to the paths created by classical GIS MA on the Imotski area. In MA, the essential question was whether the selected criteria and the assigned weight reflects what affects the cyclist's choice of bicycle route in reality. The higher weight was assigned to the slope grade because we considered it the most crucial criterion for bicycle route usage, especially for amateur cyclists.  
The paths created by two NNs matched well with the paths created by GIS MA. Thus, we concluded that (1) the criteria for GIS MA was well chosen, and (2) that no overfitting is present in our NN model. If overfitting were present in these two networks, they would be over-specialised on their (different) training sets, and then they would produce different predictions on the third data set, which was not the case.  
Even though the MA and NN match was close, there were still minor differences between selected routes using different methods. The differences can be affected by the fact that more criteria affect the bicycle route selection than those considered in this paper and the fact that in MA selection of criteria and weights assigned to each criterion were not perfect.