Prediction of Traffic-Violation Using Data Mining Techniques

被引：2
|
作者：
Amiruzzaman, Md [1 ]
机构：
[1] Kent State Univ, Kent, OH 44242 USA
来源：
PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2018, VOL 1 | 2019年 / 880卷
关键词：
Traffic; Prediction; Crime; Violations; Data mining; CRIME;
D O I：
10.1007/978-3-030-02686-8_23
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents the prediction of traffic-violations using data mining techniques, more specifically, when most likely a traffic-violation may happen. Also, the contributing factors that may cause more damages (e.g., personal injury, property damage, etc.) are discussed in this paper. The national database for traffic-violation was considered for the mining and analyzed results indicated that a few specific times are probable for traffic-violations. Moreover, most accidents happened on specific days and times. The findings of this work could help prevent some traffic-violations or reduce the chance of occurrence. These results can be used to increase cautions and traffic-safety tips.
引用
收藏
页码：283 / 297
页数：15
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