目标

Question

目标

我想使用横向位置数据找到图中所示车辆变道的持续时间。

数据

以下是仅有一种车辆的数据：

> dput(a)
structure(list(Frame.ID = 526:1058, xcoord = c(14.346, 14.367, 
14.388, 14.419, 14.458, 14.503, 14.55, 14.6, 14.65, 14.702, 14.754, 
14.807, 14.86, 14.913, 14.966, 15.02, 15.072, 15.125, 15.178, 
15.23, 15.282, 15.333, 15.384, 15.434, 15.482, 15.529, 15.574, 
15.617, 15.657, 15.694, 15.727, 15.755, 15.78, 15.802, 15.823, 
15.841, 15.858, 15.874, 15.889, 15.903, 15.917, 15.93, 15.942, 
15.955, 15.967, 15.978, 15.989, 16, 16.011, 16.022, 16.033, 16.044, 
16.055, 16.065, 16.075, 16.085, 16.095, 16.104, 16.112, 16.12, 
16.129, 16.139, 16.151, 16.164, 16.178, 16.195, 16.212, 16.231, 
16.25, 16.27, 16.291, 16.312, 16.333, 16.356, 16.379, 16.403, 
16.428, 16.455, 16.482, 16.511, 16.542, 16.574, 16.609, 16.646, 
16.687, 16.732, 16.783, 16.839, 16.902, 16.967, 17.033, 17.1, 
17.168, 17.232, 17.294, 17.354, 17.41, 17.464, 17.513, 17.559, 
17.6, 17.636, 17.665, 17.685, 17.694, 17.7, 17.708, 17.725, 17.751, 
17.782, 17.817, 17.856, 17.897, 17.939, 17.982, 18.025, 18.067, 
18.108, 18.145, 18.178, 18.207, 18.232, 18.255, 18.274, 18.292, 
18.308, 18.323, 18.336, 18.349, 18.361, 18.372, 18.383, 18.393, 
18.403, 18.413, 18.422, 18.432, 18.441, 18.451, 18.46, 18.469, 
18.479, 18.488, 18.496, 18.505, 18.513, 18.521, 18.529, 18.537, 
18.544, 18.55, 18.556, 18.562, 18.567, 18.574, 18.58, 18.588, 
18.597, 18.609, 18.623, 18.64, 18.662, 18.69, 18.722, 18.76, 
18.802, 18.849, 18.899, 18.953, 19.012, 19.076, 19.144, 19.218, 
19.299, 19.386, 19.479, 19.574, 19.669, 19.763, 19.855, 19.945, 
20.031, 20.112, 20.187, 20.254, 20.31, 20.352, 20.385, 20.412, 
20.435, 20.45, 20.455, 20.449, 20.436, 20.416, 20.39, 20.361, 
20.328, 20.293, 20.256, 20.217, 20.178, 20.139, 20.1, 20.063, 
20.026, 19.99, 19.957, 19.925, 19.895, 19.867, 19.842, 19.819, 
19.796, 19.774, 19.751, 19.729, 19.707, 19.685, 19.662, 19.64, 
19.617, 19.594, 19.571, 19.547, 19.523, 19.499, 19.473, 19.449, 
19.426, 19.404, 19.382, 19.359, 19.336, 19.312, 19.288, 19.263, 
19.237, 19.211, 19.184, 19.156, 19.127, 19.097, 19.066, 19.033, 
18.998, 18.961, 18.921, 18.878, 18.831, 18.781, 18.727, 18.67, 
18.612, 18.554, 18.498, 18.446, 18.397, 18.349, 18.304, 18.264, 
18.233, 18.21, 18.194, 18.182, 18.175, 18.171, 18.17, 18.172, 
18.177, 18.183, 18.192, 18.202, 18.213, 18.226, 18.241, 18.258, 
18.277, 18.298, 18.321, 18.346, 18.371, 18.396, 18.422, 18.447, 
18.471, 18.495, 18.518, 18.54, 18.559, 18.577, 18.591, 18.601, 
18.606, 18.605, 18.6, 18.593, 18.584, 18.579, 18.58, 18.59, 18.607, 
18.629, 18.655, 18.682, 18.711, 18.739, 18.766, 18.792, 18.818, 
18.842, 18.864, 18.885, 18.905, 18.924, 18.943, 18.961, 18.98, 
19, 19.02, 19.038, 19.054, 19.068, 19.081, 19.092, 19.103, 19.112, 
19.121, 19.129, 19.137, 19.144, 19.15, 19.156, 19.161, 19.166, 
19.169, 19.172, 19.173, 19.173, 19.171, 19.168, 19.163, 19.156, 
19.147, 19.136, 19.123, 19.109, 19.093, 19.078, 19.061, 19.041, 
19.017, 18.988, 18.954, 18.918, 18.878, 18.836, 18.795, 18.756, 
18.722, 18.693, 18.671, 18.655, 18.642, 18.633, 18.625, 18.619, 
18.613, 18.608, 18.602, 18.593, 18.58, 18.562, 18.537, 18.504, 
18.46, 18.403, 18.33, 18.234, 18.115, 17.972, 17.806, 17.623, 
17.427, 17.223, 17.013, 16.802, 16.592, 16.389, 16.191, 15.998, 
15.806, 15.604, 15.386, 15.149, 14.891, 14.617, 14.328, 14.029, 
13.722, 13.412, 13.097, 12.773, 12.436, 12.084, 11.723, 11.361, 
11.006, 10.663, 10.334, 10.02, 9.723, 9.453, 9.219, 9.027, 8.874, 
8.753, 8.657, 8.583, 8.525, 8.481, 8.448, 8.421, 8.4, 8.384, 
8.371, 8.36, 8.351, 8.345, 8.338, 8.33, 8.319, 8.304, 8.284, 
8.258, 8.224, 8.183, 8.136, 8.084, 8.029, 7.971, 7.912, 7.853, 
7.794, 7.736, 7.681, 7.629, 7.581, 7.54, 7.506, 7.482, 7.468, 
7.46, 7.459, 7.462, 7.468, 7.477, 7.489, 7.501, 7.514, 7.526, 
7.539, 7.55, 7.562, 7.573, 7.584, 7.595, 7.607, 7.62, 7.636, 
7.654, 7.675, 7.702, 7.734, 7.773, 7.823, 7.885, 7.96, 8.046, 
8.134, 8.213, 8.278, 8.322, 8.342, 8.338, 8.308, 8.258, 8.192, 
8.112, 8.023, 7.927, 7.827, 7.725, 7.623, 7.522, 7.424, 7.334, 
7.252, 7.183, 7.128, 7.093, 7.078, 7.085, 7.117, 7.177, 7.267, 
7.385, 7.525, 7.679, 7.839, 8, 8.155, 8.296, 8.418, 8.519, 8.606, 
8.682, 8.749, 8.82, 8.891, 8.956, 9.012, 9.057, 9.09, 9.126, 
9.162, 9.197, 9.227, 9.249, 9.257, 9.254, 9.251, 9.247), Lane = c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L)), row.names = c(NA, -533L), .Names = c("Frame.ID", 
"xcoord", "Lane"), class = c("tbl_df", "tbl", "data.frame"))

方法

为了估计车道变更持续时间，我想从第一次报告目标车道号的行开始。在该示例中，目标车道是1并且当车辆接触路面标记时第一次报告数字1，如图中所示。这一点可以添加到数据中：

a$rows <- as.numeric(row.names(a)) # Row numbers
a$lch <- a$xcoord[match(head(which(a$Lane==1),1), a$rows)]

对于这一点，我想找到与其他每一点的区别。

a$difference <- abs(a$lch - a$xcoord)

然后，比较两个方向的差异，将会有一些行，其中差异将非常相似，表明横向位置几乎相似。两个方向的第一个实例将被视为车道变换机动的极限如何在R中实现这个比较部分？我不知道如何从Frame.ID 929（此示例）向上和向下“移动”以比较差异变得相似的位置。

Answer 1

不确定它是否是最佳/最快的解决方案，但我们的想法是计算两个连续的差异＆＃34;差异＆＃34;您创建的列，并选择一个小值作为阈值，以标记它们开始变得相似。然后得到最接近每个车道变化时间点的行。

请注意，我将您的数据集重命名为dt。逐步运行该过程以查看该过程的工作原理。如果您对此感到满意，可以通过组合一些命令来缩短脚本。

dt$rows <- as.numeric(row.names(dt)) # Row numbers
dt$lch <- dt$xcoord[match(head(which(dt$Lane==1),1), dt$rows)]
dt$difference <- abs(dt$lch - dt$xcoord)

library(dplyr)

dt %>%
  mutate(frameIDchange = Frame.ID[difference==0],                # spot the frame ID of the change
         diff_diff = difference - lag(difference, default=difference[1]),   # find the difference of two consequtive differences
         flag =  ifelse(abs(diff_diff) <= 0.01,1,0)) %>%         # flag if the difference of two consequtive differences is equal or less to our threshold (0.01)
  filter(flag==1) %>%                                            # keep only rows with threshold difference
  mutate(frameIDdiff = abs(Frame.ID-frameIDchange)) %>%          # calculate distance of frame IDs
  group_by(Lane) %>%                                             # for each lane
  filter(frameIDdiff== min(abs(frameIDdiff)))                    # return the frame id closest to the change frame id with a difference equal or less to our threshold


#   Frame.ID xcoord Lane rows    lch difference frameIDchange diff_diff flag frameIDdiff
# 1      896 18.593    2  371 12.436      6.157           929    -0.009    1          33
# 2      953  8.351    1  428 12.436      4.085           929     0.009    1          24

这告诉您，差异开始变得相似的帧ID（0.01阈值）分别为896和953，分别为2和1。显然，更改的帧ID（929）属于这些帧之间的预期。

您还可以尝试稍高/更低的阈值来查看结果如何变化。

如何确定R中特定行周围的相似值？

目标

数据

方法

1 个答案: