基于LSTM的电子滑板车轨迹特征的分类,用于单人与双人骑行检测
Jiahui Zhao1, Jiaming Wu2, Sida Jiang3
1School of Transportation, Southeast University, Nanjing, China.
Traffic injury prevention
|July 17, 2025
概括
这项研究使用长短期记忆 (LSTM) 网络识别了不安全的电动滑板车 (e-scooter) 骑行行为. 该模型准确地对骑行模式进行分类,使实时风险检测成为更安全的城市交通.
科学领域:
- 城市流动性和交通安全.
- 在行为分析中的机器学习应用.
- 微移动性风险评估 微移动性风险评估
背景情况:
- 无电动滑板车 (e-scooter) 在城市短途旅行中越来越受欢迎.
- 不安全的骑行行为对骑手和公众构成重大风险.
- 需要识别和减轻这些不安全的行为,以提高安全性.
研究的目的:
- 用数据驱动方法对单人和双人电动滑板车骑行行为进行分类.
- 为了确定关键的动态轨迹特征,表明不安全的骑行.
- 开发一个实时风险检测和安全干预在共享微型出行中的系统.
主要方法:
- 利用来自瑞典哥德堡电动滑板车的轨迹数据.
- 使用长期短期记忆 (LSTM) 神经网络进行动态时间特征分析.
- 优化输入序列长度为240秒,以提高计算效率和预测准确度.
主要成果:
- LSTM模型实现了高性能:92.65%的准确性,91.69%的精度,93.85%的回忆,95.56%的F1得分和0.9169的AUC.
- 与RNN和随机森林等传统模型相比,显示出显著的性能优势.
- 确定了加速,转角度,速度和启动电荷状态 (SOC) 作为关键特征.
结论:
- 提出的基于LSTM的方法有效地对电动滑板车骑行行为进行了分类.
- 这种方法有助于城市当局和运营商实时检测风险和安全干预.
- 有助于开发更安全的共享电动滑板车系统和城市微型移动.
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