Predict the Number of Traffic Accidents in Turkey by Using Machine Learning Techniques and Python Tools
Traffic accidents have undergone rapid growth in recent years worldwide, causing great life and property losses. Therefore, predicting traffic accidents is a crucial problem for improving transportation and public safety. Machine learning is one branch of artificial intelligence that can extract information from data and use statistical methods to predict values. In this paper, three machine-learning techniques were applied to estimate the number of traffic accidents (with death or injury) in Turkey until the year 2029, which are: Linear Regression (LR), Decision Trees (DT), and Random Forest (RF). These techniques were tested using a real data set obtained from the website: http://www.tuik.gov.tr and we got the result that Linear Regression (LR) has the best performance. This result indicates the approach's predictive superiority in predicting road accidents. As a result, the study will assist road transport and insurance agencies in developing road safety strategies.
Copyright (c) 2022 Artificial Intelligence Studies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Artificial Intelligence Studies (AIS) publishes open access articles under a Creative Commons Attribution 4.0 International License (CC BY). This license permits user to freely share (copy, distribute and transmit) and adapt the contribution including for commercial purposes, as long as the author is properly attributed.
For all licenses mentioned above, authors can retain copyright and all publication rights without restriction.