Applicera maskininlärning på vägtrafikdata för att klassificera gatutyper i Stockholm

Sammanfattning: In this thesis, two different machine learning models have been applied on road traffic data from two large cities in Sweden: Gothenburg and Stockholm. The models have been evaluated with regard to classification of street types in urban environments. When planning and developing road traffic systems it is important that there is reliable knowledge about the traffic system. The amount of available traffic data from urban areas is growing and to gain insights about historical, current and future traffic patterns the data can be used for traffic analysis. By training machine learning models that are able to predict what type of street a measuring location belongs to, a classification can be made based on historical data. In this thesis, the performance of two different machine learning models are presented and evaluated when street types are predicted and classified. The algorithms used for the classification were K-Nearest Neighbor and Random Forest which were applied to different combinations of attributes. This was done in order to identify which attributes that lead to the optimal classification of street types in Gothenburg. For training the algorithms the dataset consisted of traffic data collected in Gothenburg. The final model was applied on the traffic data in Stockholm and hence the prediction of street types in that area were obtained. The results of this study show that a combination of all tested attributes leads to the highest accuracy and the model that obtained these results was Random Forest. Even though there are differences between topography and size of the two cities, the study leads to relevant insights about traffic patterns in Stockholm.