一个实时交通风险预测框架,包括成本敏感的学习和动态值
1School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China; School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore.
Accident; analysis and prevention
|May 6, 2025
概括
本研究引入了成本敏感的学习和动态值,通过考虑错误分类成本和提高多类性能来提高交通安全,提高实时交通风险预测的准确性.
科学领域:
- 交通安全工程 交通安全工程
- 机器学习应用程序 机器学习应用程序
- 数据驱动风险评估数据驱动的风险评估
背景情况:
- 现实车辆轨迹数据用于实时交通风险预测.
- 现有的方法忽略了错误分类成本和不同的后果,影响了可靠性.
- 交通风险预测需要提高准确性和可靠性,以进行主动的安全管理.
研究的目的:
- 将交通风险分为四个级别 (没有风险,低风险,中风险,高风险).
- 使用成本敏感学习 (CSL) 纳入错误分类成本.
- 通过动态值 (DTs) 提高多类预测性能,解决类不平衡问题.
主要方法:
- 利用来自HighD数据集的真实车辆轨迹数据.
- 集成的CSL和DTs与四个基线机器/深度学习模型.
- 使用遗传算法 (GA) 来优化成本系数和值.
主要成果:
- 与基线模型相比,基于CSL-DTs的模型在多类交通风险预测方面表现优越.
- 拟议模型的计算时间适合实时应用.
- 稳定性分析证实了模型稳定性和GA优化的可靠性.
结论:
- 拟议的CSL-DTs方法显著提高了实时交通风险预测的可靠性.
- 这些发现支持推进积极的交通安全管理策略.
- 该研究为准确和成本意识的交通风险评估提供了一个强大的框架.
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