Predicting traffic accidents and their injury severities using machine learning techniques

H. L. Gururaj; V. Janhavi; U. TanujaFransesco FlaminiB. C. SoundaryaVinayakumar Ravi

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Predicting traffic accidents and their injury severities using machine learning techniques

机译：Predicting traffic accidents and their injury severities using machine learning techniques

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Traffic accidents are among the most censorious issues confronting the world as they cause numerous deaths, wounds and fatalities just as monetary misfortunes consistently. According to the world health organization (WHO) reports, 5,18,3626 accidents took place in India in the year 2019. Factors that contribute to these road crashes/ traffic accidents and resulting injuries include inattentive drivers, unenforced traffic laws, poor road infrastructure, driving in bad weather conditions and others. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. Machine learning models can be applied to model and predict the severity of injury that occurs during road accidents. one such way is to apply unsupervised learning models such as Apriori, Apriori TID (transaction id), SFIT (set operation for frequent itemset using transaction database) and ECLAT (equivalence class clustering and bottom-up lattice traversal) which analyze the unlabeled traffic accidents dataset and determine the relationship between traffic accidents and injury. This research work is helpful for traffic departments to decrease the number of accidents and to distinguish the injury's seriousness extensive simulations were carried out to demonstrate the un-supervised learning algorithms for predicting the injury severity of traffic accidents. Apriori algorithm predicts the patterns in 962 milliseconds, Apriori TID (transaction id) algorithm predicts the pattern in 557 milliseconds, SFIT algorithm predicts the pattern in 516 milliseconds and ECLAT algorithm predicts the pattern in 124 milliseconds. ECLAT algorithm took less time compared to all the other algorithms.

机译：交通事故是最挑剔的世界,因为他们面对的问题原因许多人死亡,伤和死亡货币的不幸。世界卫生组织(世卫组织)报告,3626年5日,18日事故发生在印度2019年。崩溃/交通事故和造成伤害包括粗心的司机,未执行的流量法律,道路基础设施差,驾驶在恶劣天气状况等。努力建立模型来选择一组影响因素,建立模型受伤的严重程度进行分类。模型可以应用于模型和学习预测期间发生伤害的严重程度交通事故。无监督学习模型如先验的、先验的TID(事务id), SFIT(设置操作频繁项目集使用事务数据库)和辉煌的成就(等价类集群和自底向上的晶格遍历)分析交通事故无标号数据集,并确定交通事故和之间的关系受伤。部门减少事故的发生和区分损伤的严重性广泛的模拟进行了证明了联合国监管学习算法预测交通伤害的严重程度事故。模式在962毫秒内,先天TID(事务id)算法预测模式在557毫秒,SFIT算法预测模式在516毫秒内和辉煌的算法预测模式在124毫秒。算法比所有的花更少的时间其他算法。

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来源
《International Journal of Transport Development and Integration》 |2022年第4期|363-377|共15页
作者
H. L. Gururaj; V. Janhavi; U. TanujaFransesco FlaminiB. C. SoundaryaVinayakumar Ravi;
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作者单位

Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, India;

Vidyavardhaka College of Engineering, India;

IDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, SwitzerlandCenter for Artificial Intelligence, Prince Mohammad Bin Fahd University, Saudi Arabia;

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正文语种英语
中图分类计量学;
关键词
Apriori; ECLAT; machine learning; traffic accidents;

机译：先验;ECLAT;机器学习;交通事故;

Predicting traffic accidents and their injury severities using machine learning techniques

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