基于极端价值理论的撞车频率预测,使用路边激光雷达 (LIDAR) 基于车辆轨迹数据的车辆轨迹数据
Nischal Bhattarai1, Yibin Zhang1, Hongchao Liu1
1Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Accident; analysis and prevention
|September 28, 2023
概括
这项研究使用路边LiDAR数据来识别接近碰撞的情况,改进了碰撞预测模型. 将替代安全措施与极端价值理论相结合,为交通安全分析提供了积极的方法.
科学领域:
- 交通安全工程 交通安全工程
- 运输数据分析 运输数据分析
- 激光雷达 (LiDAR) 技术的应用
背景情况:
- 传统的崩预测模型 (CPM) 通常依赖于不可靠的历史崩数据.
- 接近碰撞的预测提供了一种积极的方法来提高交通安全.
- 路边LiDAR提供高分辨率的车辆轨迹数据,用于详细的运动分析.
研究的目的:
- 开发一种方法,使用路边LiDAR数据识别接近碰撞的情况.
- 应用替代安全措施和极端价值理论 (EVT) 来估计碰撞概率.
- 评估不同替代测量对在预测碰撞频率方面的有效性.
主要方法:
- 使用路边LiDAR收集微观车辆轨迹数据.
- 使用替代指标识别了接近碰撞的情况:碰撞时间 (TTC),后入侵时间 (PET),预期碰撞时间 (ACT) 和最大减速 (MaxD).
- 应用双变极值理论 (EVT) 来结合基于时间和基于逃避行动的替代措施来预测碰撞概率.
主要成果:
- 与无变量模型相比,双变量EVT模型更适合冲突极端,并改善了碰撞频率预测.
- 在双变量模型中,ACT和MaxD对表现出最高的准确性.
- 这对TTC和MaxD有效地反映了十字路口的相对威胁水平.
结论:
- 拟议的方法允许使用路边LiDAR数据对信号交叉路口进行主动安全分析.
- 将代用安全措施与双变量EVT相结合,可以提高接近撞车预测的准确性.
- 这种方法为数据驱动的交通安全改进提供了基础.
相关概念视频
01:08Determination of Expected Frequency
2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
01:24Rapidly Varying Flow
93
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
93
01:15Elastic Collisions: Case Study
14.2K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.2K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:04Random Error
922
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
922