Identification and behavioral analysis of high-risk riders of shared bicycles using artificial intelligence in geospatial data

Due to bike-sharing regulations as well as the rules of insurance companies, users are not allowed to use bicycles on highways, tunnels, and bridges, so identifying those who violate these rules is of particular importance. In this study, we first attempt to identify high-risk users using the spatial join method and identify the effective factors to detect this abuse. Then, by classifying different machine learning methods, it is possible to classify high-risk users according to travel and user characteristics so as to deal with repeat offenders. Finally, after trying several methods based on the model's accuracy, the best method is selected. The results show, the best way to classify high-risk users is logistic regression and LDA1, which gives us an accuracy of about 85%. Finally, we analyze the travel characteristics and personality of high-risk users and compare these results with those of ordinary users.

Identification and behavioral analysis of high-risk riders of shared bicycles using artificial intelligence in geospatial data Keywords:

Bike Sharing System , High-Risk Users , Behavior Assessment , Geodata Science , Artificial Intelligence.