# Using Neural Networks for Bicycle Route Planning
## 2 Previous Work  
The subject of bicycle route planning is beginning to appear in the literature in the past decade. The literature search process started with using the MA, in which we searched for previous usages of MA in route selection and continued with the literature review in NN usage with GIS. Our previous work [6] showed that GIS could be employed in a rural area to produce bicycle routes based on MA analysis. [PERSON] and [PERSON] presented the bicycle route suitability assessment based on MA [7]. To determine the criteria for the route selection, the authors used the geographic information system and the expert opinion survey employed for each criterion weight calculation, combined with the fuzzy analytic hierarchy process (FAHP). It is a practical tool in criteria determination for imprecise semantics [8; 9]. After using FAHP to interpret the expert opinion survey, the acquired results were used as an input for calculating the most suitable route. In this work, ref. [7] [PERSON] and [PERSON] concluded that GIS usage is helpful for comprehensive model evolvent for bicycle network planning. [PERSON], [PERSON], and [PERSON] in [10] analysed which criteria cyclists paid the most excellent attention to when choosing their route. As a source of cyclist tracking data, they used the GPS data from the mobile application \"Cycletracks\" for the Seattle area (WA, USA). One or more factors influenced the selection of a bicycle route. There are three groups in which the factors can be assigned: convenience-related factors (e.g., stopover points, bike racks and lockers availability, trip distance), safety-related factors (e.g., street lighting, speed limits, bicycle facility type) and leisure-related factors (e.g., parks, street trees shading, distance to mountains and water bodies, monumental places of interests). Some factors can relate to all three groups: slope degree, pavement quality, and the number of intersections with signalisation. The GPS data were gleaned for three and a half years [9]. A sum of 4.9 million GPS points was used to produce 3310 routes. The analysis detected safety as an essential bicycle route choice factor. A second important criterion was leisure-related--the cyclist chooses routes located near green areas and water. [PERSON] and [PERSON] [11] created a process of bicycle routes selection using GIS and applied it to the city of Berkeley. This procedure consisted of creating a database, choosing the most suitable path between each start location-end location pair, and gathering results to pick out the most suitable routes. The criteria authors selected to determine a bicycle route desirability are the travel duration, the road traffic, the slope grade, and the road surface conditions. The travel time included intersection delay and distance; road traffic included air pollution and traffic conflicts. The road surface conditions included the smoothness of the road and its width. This paper concluded that GIS is helpful for bicycle route choice since it can use different available sources and integrate them. GIS is also essential because of its possibility to capture spatial criteria, which is essential to cycling. An interesting approach is shown in [12], where [PERSON] et al. considered different road users in an urban area. They propose a unifying NN model for time and energy-efficient routes calculations within an urban road network. Authors had ecological motivation since their case study in Beijing showed a reduction of average travel time by 20% and energy consumption by 10%. Interesting usage of NNs and GIS is shown in [13], where the author checks the success rate of NN in predictions of landslides using GIS tools and technologies. The performance of such a network is 71.3% which the author considered as encouraging. In [14], the authors showed a principle in which they used both static and dynamic information in route planning among obstacles originating from a forest fire.