在混合交通条件下的不受控制的十字路口的行人困境区的估计和建模
Govinda Lalam1, Ravishankar K V R2
1Department of Civil Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, India.
Traffic injury prevention
|October 17, 2024
概括
这项研究模拟了在不受控制的十字路口的行人困境区 (PDZ). 支持向量机 (SVM) 比累积差距分布 (GCD) 更好地估计了PDZ边界,年龄,性别和车辆速度等因素影响了边界.
科学领域:
- 交通工程是交通工程.
- 交通运输中的人类因素
- 城市规划 城市规划
背景情况:
- 对于分析行人困境区 (PDZ) 来说,行人差距接受 (PGA) 是至关重要的.
- 由于PDZ中的行人决策,不受控制的十字路口带来了重大风险.
- 建模PDZ边界对于改善行人安全和PGA理论至关重要.
研究的目的:
- 量化和建模在不受控制的X交叉点的行人困境区 (PDZ) 边界.
- 为了比较累积差距分布 (GCD) 和支向量机 (SVM) 方法在估计PDZ边界方面的有效性.
- 在混合交通条件下确定影响PDZ边界界的因素.
主要方法:
- 从印度四个不受控制的十字路口收集了视频数据.
- 使用了DataFromSky软件和手动提取行人和车辆数据.
- 使用累积差距分布 (GCD) 和支持向量机 (SVM) 进行PDZ边界估计.
- 开发了一种二进制物流回归模型来确定PDZ边界的影响.
主要成果:
- 与GCD相比,SVM方法提供了更准确的PDZ边界估计.
- 据估计,PDZ的下边界为6.0米 (SVM) 和9.0米 (GCD);上边界为18.5米 (SVM) 和16.5米 (GCD).
- 步行者的年龄,性别,穿越速度,穿越位置,车辆类型和接近速度与PDZ边界极限显著相关.
结论:
- 支持向量机 (SVM) 是估计PDZ边界的高级方法.
- 根据不同的人口统计数据,PDZ的边界会发生变化 (例如,女性和年长的行人会更进一步).
- 车辆的特性 (例如,两轮车) 和较低的车速会影响行人间隙的接受度和PDZ的大小.
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