一种基于时空特征的方法,用于在城市地区检测未经许可的出租车
Yun Xiao1, Rongqiao Li2, Jinyan Li1
1School of Urban Construction and Transportation, Hefei University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究引入了一种使用车辆旅行数据识别未经许可的出租车的方法,大大提高了运输安全和市场秩序. 开发的模型准确地检测到这些车辆,从而实现了有针对性的交通管理策略.
科学领域:
- 运输工程 运输工程
- 数据科学数据科学数据科学
- 公共安全公共安全.
背景情况:
- 没有执照的出租车扰乱了运输市场,并带来了安全风险.
- 有效的识别方法对于监管执法至关重要.
研究的目的:
- 根据旅行特征提出和验证一种识别未经许可的出租车的方法.
- 分析疑似未经许可的出租车的时空分布.
主要方法:
- 从交通监控数据计算车辆里程和运行时间.
- 应用差异分析并建立数学识别模型.
- 使用统计测试 (霍斯默-莱梅肖,混矩阵,ROC曲线) 和地理信息匹配验证模型.
主要成果:
- 开发的模型有效地识别可疑的未经许可的出租车,准确率为99.10%.
- 与私家车相比,未经许可的出租车的每日里程,运行时间和运行日数显著更高.
- 确定了旅行起源,目的地和时间的时空模式.
结论:
- 拟议的方法为识别未经许可的出租车提供了有效的工具.
- 调查结果使交通管理当局能够实施有针对性的监管措施.
- 改进的识别提高了运输市场秩序和乘客安全.
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