# Using Neural Networks for Bicycle Route Planning
## 2 Previous Work
In addition, the authors provided an extended A* algorithm for obstacle-avoiding route calculation with consideration to the predicted information apropos roads state, departure time, and speed of vehicles. They concluded that with the usage of the extended algorithm, among dynamic data provided by fire simulations, a safe path to the destination was calculated. The usage of NNs increased in recent years because of their wide area of application. NNs are increasingly applied in managing transportation infrastructures. In [15], [PERSON] combined NN with GIS systems for road accident predictions. He compared the results of NN prediction with the multivariate analysis (MVA) and showed that both techniques provided good predictions. Similarly, in the paper [16], [PERSON] and [PERSON] used recurrent neural networks (RNN) to predict the traffic accidents' seriousness. The RNNs are NNs containing feedback connections designed for sequence modelling. Feedback connections assure the past activation memory, which provides the learning dynamics of sequential data. The authors concluded that the RNN model achieved the best accuracy in traffic accident injury severity. The paper [17] was dealing with an optimisation model for slowing down the congestion in urban traffic; [PERSON] and [PERSON] used ArcGIS technology and MATLAB to optimise the bus line around the scope of population distribution. Another exciting application of GIS and NN usage combination is shown by [PERSON] et al. in [18]. A flood model was developed with diverse flooding causative factors using GIS and NN techniques for flood-prone areas simulation. In model training, seven input nodes were used, each representing parameters which cause a flood. They concluded that this model is accurate enough and might provide a significant advantage along with a warning system operating in real-time. [PERSON] et al. [19] combined GIS with ANN for road safety risk analysis; a new methodological approach in road safety risk evaluation is presented. ANN was used to calculate the predictability of risk value, while GIS was used to produce geospatial output. The authors concluded that ANN with GIS provided an efficient and easy output in road safety data analysis and safety improvement decision making. [PERSON], [PERSON] and [PERSON] in [20] presented bicycle level of service (BLOS)-GIS-based indicators to estimate link suitability in transport networks. The authors concluded that the BLOS method could assist in bicycle route generation, but the number of generated unique routes is low. [PERSON] et al. [21] presented the integration of GIS with artificial intelligence for modelling urban evolution. For clusters representing the level of urbanisation level creation, the authors used the fuzzy c-means algorithm. With the usage of the produced clusters, authors could monitor region development from a rural area to an urban area with a large population. The authors registered the final results in a geodatabase and used GIS software to visualise them. NNs were used for urban evolution prediction by observing the changes in the region's neighbouring area. The network is trained to learn how individual units evolve and what is the influence of these changes on the neighbouring areas. The authors concluded that the combination of GIS, fuzzy logic, and NN with the proposed methodology, along with a complete database, delineates the future urbanism level more reliably.