为高速公路特定时间安全性能函数的估计计算完整数据
Jingwan Fu1, Mohamed Abdel-Aty1, Xin Yan1
1Department of Civil, Environmental, and Construction Engineering, Department of Statistics and Data Science, University of Central Florida (UCF), Orlando, FL 32816-2450, United States.
Accident; analysis and prevention
|June 26, 2023
概括
本研究引入了一种代归算方法,以填补缺少的交通数据,以开发精确的特定时间的安全性能函数 (SPF). 该方法成功地重建了交通模式,即使没有完整的数据,也能够实现可靠的撞车预测模型.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 数据科学数据科学数据科学
背景情况:
- 准确的碰撞频率预测需要特定时间的安全性能功能 (SPF).
- 在许多州,高分辨率的交通数据 (体积和速度) 往往缺失或没有归档,阻碍了SPF的开发.
- 现有的方法缺乏强大的解决方案来归因完整的流量数据集.
研究的目的:
- 提出和验证一种新的代归算方法,用于重建缺失的交通量和速度数据.
- 允许在缺乏完整交通数据的州开发特定时间的SPF.
- 在交通建模中评估归算数据的准确性和有效性.
主要方法:
- 计算了18个州的撞车率,并使用单向ANOVA对类似撞车率的州进行分组.
- 开发并测试了一种使用佛罗里达州 (FL) 和弗吉尼亚州 (VA) 交通数据的代归算方法.
- 使用平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 与实际收集的数据对比,验证了归算数据.
主要成果:
- 代归算方法成功捕获了与真实数据可比的交通模式.
- 归算Ln体积的MAE为2.47辆/段/3小时;归算Ln平均速度的MAE在FL为1.36英里/小时.
- 归算Ln体积的MAPE为11.07%;归算Ln平均速度的MAPE为FL的7.40%.
- 使用归算数据开发的特定时间的SPF在早晨峰值模型中实现了87.1%的预测准确度.
结论:
- 提出的代归算方法对于重建缺失的交通数据是有效的.
- 导入的数据可以可靠地用于开发精确的特定时间的安全性能功能.
- 这种方法解决了数据缺口,并促进了数据稀缺地区的动态崩预测建模.
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