Data availability from real applications is not a concern anymore, nevertheless the extraction of necessary information is still the challenge. Identifying the areas that contain the highest number of crashes within a city may be of particular interest for urban mobility and risk prevention. Also, having areas of the city classified according to the risk of crashes (areas with high presence of road bumps, uneven road surfaces, etc.) would allow companies to apply targeted business policies, to minimize maintenance costs and the deterioration of electric motorbikes. In this work, artificial intelligence algorithms and time series geographical clustering techniques are investigated in order to classify the risk areas of the city of La Coruna in Spain.
Towards Machine Learning Enabled Analysis of Urban Mobility of Electric Motorbike: A Case Study for Improving Road Manteinance and Driver’s Safety in La Coruna City
Di Martino B.;Venticinque S.;
2022
Abstract
Data availability from real applications is not a concern anymore, nevertheless the extraction of necessary information is still the challenge. Identifying the areas that contain the highest number of crashes within a city may be of particular interest for urban mobility and risk prevention. Also, having areas of the city classified according to the risk of crashes (areas with high presence of road bumps, uneven road surfaces, etc.) would allow companies to apply targeted business policies, to minimize maintenance costs and the deterioration of electric motorbikes. In this work, artificial intelligence algorithms and time series geographical clustering techniques are investigated in order to classify the risk areas of the city of La Coruna in Spain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
