二次崩的空间和时间预测结合了堆叠的稀疏自动编码器和长的短期记忆
Haojie Li1, Qi Gao1, Ziqian Zhang1
1School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
Accident; analysis and prevention
|July 6, 2023
概括
预测二次碰撞地点对于交通安全至关重要. 一个新的深度学习模型,SSAE-LSTM,准确地预测了这些崩的发生地点和时间,帮助预防策略.
科学领域:
- 运输工程 运输工程
- 深度学习应用程序
- 交通安全研究 交通安全研究
背景情况:
- 二次撞车造成严重的交通延误和安全风险.
- 现有的研究主要集中在二次撞车可能性上,而不是精确的位置预测.
- 准确的时空预测可以为主动的安全干预和资源部署提供信息.
研究的目的:
- 开发二次撞击的空间和时间位置的预测模型.
- 通过实施有针对性的预防策略,提高交通安全.
- 为应急响应和交通管理提供见解.
主要方法:
- 开发了一种混合深度学习模型,即堆叠稀疏自动编码器 (SSAE) 与长短期记忆 (LSTM) 网络 (SSAE-LSTM) 相结合.
- 来自加利福尼亚州I-880高速公路 (2017-2021) 的交通和撞车数据被利用.
- 使用速度轮地图方法确定了次要撞车;使用5分钟的交通变量建模了时空差距.
主要成果:
- 在空间和时间二次撞击预测方面,SSAE-LSTM模型显著超过了基准模型 (PCA-LSTM,SSAE-SVM,BPNN).
- SSAE4-LSTM1展示了卓越的空间预测准确度.
- SSAE4-LSTM2显示出出色的时间预测能力,联合评估证实了整体准确性.
结论:
- 拟议的SSAE-LSTM混合深度学习模型为预测二级机地点提供了强大的解决方案.
- 这种预测能力可以指导实施有效的二次事故预防措施.
- 这些发现支持数据驱动的交通安全管理和紧急响应优化.
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