# Using Neural Networks for Bicycle Route Planning
## 6 Conclusions  
Bicycle route planning with NN usage is presented in this paper. A set of already established bicycle routes is used for the NN training, which enabled the transfer of bicycle route design human expertise into a NN. Then the trained NN is used to design the routes on a novel area. We trained NNs in two different areas which were capable of choosing the best route among the given road network. This paper dealt with a different approach to dealing with the most appropriate bicycle route in a rural area. In literature research, most of the papers dealt with bicycle routing problems in an urban area but primarily relied on GIS or NN separately. This is the reason why we decided to join these two approaches. While MA needed an expert to decide which criteria should be used, the NN approach made it possible to have expertise concentrated in NN. It is crucial that we have shown that the model is usable not only in the Imotski area but also in the area of the Dalmatian hinterland, maybe even broader. The visualisation of the conclusion is shown in Figure 16.  
The NN approach showed that it could be successfully used for bicycle route prediction. Considering the roads segment data as the input in the NN, we decided to use the layer recurrent NN. We then trained two NNs on existing routes in two areas (Vrgorac and Kinin) that are similar by their characteristics. We cross-checked routes Vrgorac NN selected on existing Kinin routes and vice-versa, and they overlapped on average of 84% of their length. Besides this, we used the classical GIS multiple criteria approach to check the NN approach. The target area for our experiment of bicycle route planning was a broader area of the city of Imotski, which is a mix of small urban and rural areas. We created two routes using both modelling approaches: the first one shorter and flatter, targeted at amateurs and tourist bicyclists, and the second one longer and steeper, targeted at enthusiasts. Then we used the Kinin and Vrgorac NNs to build networks for the Imotski area and analysed how the results overlap with routes created by MA. The flat routes created with Kinin and Vrgorac NNs were identical and, compared to the GIS MA route, were 90% similar. The steeper routes of Kinin and Vrgorac were 75% similar, and the overlap with GIS MA was 80%. We can conclude that the NN approach to modelling bicycle routes is more direct and faster since, in MA, a bicycle expert has to choose criteria and adjust its weights carefully. In contrast, the NN method extracts them automatically from existing routes. Future work will include the real-world corroboration of the Imotski routes presented in this work. It is planned to use affective computing equipment to acquire data about the emotional state of cyclists who will use the chosen bicycle path, which will provide feedback about the desirability of the path in the real world. Figure 16: Diagrams of MA and NN analysis approach. Although we chose the area with highly dynamic terrain, we are unsure that the trained model would suit areas with different terrain configurations. Therefore, it would be interesting to train a model in some other areas, e.g., the Slavonia area, the vastest Croatian plain. That might give us insight into the sensitivity of our model to the terrain dynamic. The future work considering NN will be based on building a bigger training model and experimenting with NNs based on bicycle paths and not route segments. Since the safety-related factors are the most noteworthy in bicycle path selection, our work in the future will include traffic density as a criterion to evade car-congested parts of roads. J.D. : Conducted research conceptualisation, data acquisition, provided methodology, validated results, wrote the original draft, reviewed and edited draft. M.S. : supervised, advised, validated the multicriteria methodology, analysed results, reviewed and edited draft. L.K. : validated neural network methodology, analysed results, reviewed and edited draft. M.R.