基于热点的巡逻路线优化算法,用于智能警察
Dongyeon Kim1, Yejin Kan2, YooJin Aum3
1Department of Artificial Intelligence, Seoul, 04620, Korea.
Heliyon
|November 2, 2023
概括
这项研究引入了一个大数据模型,通过预测犯罪热点和优化巡逻路线来有效应对紧急情况. 其目的是通过数据驱动的犯罪预防策略来加强智能警察.
科学领域:
- 犯罪学 犯罪学
- 数据科学数据科学数据科学
- 公共政策 公共政策
背景情况:
- 智能警察,利用大数据分析,对于可持续的警察政策制定至关重要.
- 尽管认识到智能警务的必要性,但韩国警方在将数据分析结果应用于实际犯罪预防政策方面面临着挑战.
- 现有的应用程序专注于犯罪调查,例如案件联系和嫌疑人/犯罪地点预测,而不是政策实施.
研究的目的:
- 提出一种新的大数据模型,以优化紧急呼叫响应.
- 通过数据驱动的洞察力提高警察巡逻的效率.
- 弥合数据分析与预防犯罪的实际警察政策之间的差距.
主要方法:
- 通过分析社区环境数据和犯罪数据之间的关联来提取犯罪热点.
- 开发路线优化算法,以确定最短的旅行时间到预测的犯罪地点.
- 通过结合实时交通信息来评估巡逻路线的性能.
主要成果:
- 通过数据分析识别预测的犯罪活动热点.
- 为应急响应开发一个优化的路由算法.
- 考虑到动态交通条件,评估巡逻路线效率.
结论:
- 拟议的大数据模型为有效的应急响应和智能警察提供了一个框架.
- 这种模式的适应可以显著提高现有的警察巡逻系统的有效性.
- 这种数据应用模型有可能被警察部门广泛采用,以提高公共安全.
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