支持CAV的数据分析,以增强自适应信号控制安全环境
1ART-EngineS Transportation Research Laboratory, Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH 45221-0071, USA.
Accident; analysis and prevention
|September 14, 2023
概括
连接和自动驾驶汽车 (CAV) 数据增强了交通信号控制,大大降低了碰撞风险,改善了交通流动. 这种智能系统优化了十字路口的安全性和效率.
科学领域:
- 交通工程是交通工程.
- 智能运输系统 智能运输系统
- 数据融合数据融合
背景情况:
- 互联和自动驾驶汽车 (CAV) 提供高分辨率的移动数据,作为交通管理的"浮动传感器".
- 目前用于评估CAV数据的安全益处的方法由于缺乏用于自适应交通信号控制的智能数据模型而未成熟.
- 同时提高交叉路口的安全性和运营效率是交通管理中的一个复杂的挑战.
研究的目的:
- 开发和测试使用CAV轨迹进行自适应交通信号控制的智能数据融合模型.
- 整合代用安全评估模型 (SSAM) 来评估安全环境和接近碰撞的风险.
- 与传统系统相比,评估CAV生成的数据支持的自适应信号计划的安全性和运营效益.
主要方法:
- 开发了一个智能CAV生成的移动数据融合模型框架.
- 集成的SSAM参数和模型来评估碰撞风险和安全.
- 在俄俄州辛辛那提市区进行了概念验证研究,将自适应信号计划与预定时间和执行计划进行了比较.
主要成果:
- 适应信号计划显著降低了总碰撞风险高达91%,交叉碰撞71%,后端碰撞90%,变车道碰撞100%.
- 运营效率的提高包括产能增加6.8%,平均延迟减少91.49%,队列长度减少96.23%,停留次数减少75.00%.
- 虽然观察到运营效率的好处,但没有证明降低碰撞严重性的改善 (由高速标志).
结论:
- 当CAV生成的数据被融合到智能模型中时,它显示了提高十字路口安全和交通运营的巨大潜力.
- 开发的自适应交通信号控制系统,利用CAV数据和SSAM,有效降低碰撞概率并改善交通流量指标.
- 需要进一步的研究来解决碰撞严重性,并优化系统,以获得全面的安全性和效率.
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