Results

Compare means and correlations analysis were performed on all category variables (see table 2). However, we present the detailed descriptive statistics for categories that gave significant influence on mean speed. A general overview of the speed data is presented in table 3.

Table 3: Overview of the data material with loop detector number and direction.

Sites N Mean

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When examining whether trees present along the road or not affects the speed, table 4 shows that the Pearson coefficient is 𝑟 = −.032, which is very low, but indicates higher speed when trees are not present. The correlation is too small to reflect any trends, even though it is significant. Despite this weak correlation, it may be interesting to see if variables within a category gives any effect given that there are trees along the road. Table 4 presents sceneries of trees along the road that had some correlations with the speed data. There were no sceneries with trees 30-100 meters from the road.

Given trees on both sides of the road, the average speed changes depending on the trees’

distance from the road. Table 4 shows how the average speed also changes based on how dense the trees are. For the low-density scenery, the average speed is more than 5kph higher if there are trees 10-30 meters from the road than if they are less than 10 meters away. As the table shows, there were only 3 321 registrations/observations (N) with low density and the distance were 10-30 meters. The standard deviation is also quite high and the Pearson coefficient is 𝑟 = .113 which is a bit low. Even though it is statistically significant this could be explained by other things that are not included in this dataset. For the medium-density scenery, there were more registrations. The same trend with, in this case, 4.2kph higher average speed if there are trees 10-30 meters from the road than if they are less than 10 meters away. The Pearson coefficient are higher than for the low-density scenery, but still not good (𝑟 = .199). It is also statistic significant due to the large speed dataset. The third scenery is when there is high density of trees. Now the average speed is higher if the trees are less than 10 meters from the road. This time the Pearson coefficient are very low and also negative (𝑟 = −.070) which the average speed reflects. In other words, there is a very small correlation between the variable “trees distance from the road” and the speed data variable, given trees on both sides and high density.

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The table also shows that when the trees are less than 10 meters from the road, the average speed is higher when the density gets higher.

Table 4: Compare means and correlation between the speed data variable and variables of trees in different combinations

**. Correlation is significant at the 0.01 level (2-tailed).

When the trees are less than 10 meters from the road, the trees on the right side will be closer to the driving lane compared to the trees on the left side. Using the compare means function, the average speed was calculated as shown in table 4, for both medium and high density. There is a tendency of a slightly higher average speed if there were only trees on the right side, but not really a difference of significance. As the table shows, it is statistically significant but the Pearson coefficient are a quite low with the values of 𝑟 = −.116 and 𝑟 = −.088. The values

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are also negative, which indicates that the speed should have been higher when there were trees only at the left side (see table 2). As presented in table 4, the number of registrations (N) and the standard deviations are quite different, especially for the medium dens scenery.

Given trees only at the right side in the driving direction, regardless of density, table 4 shows that the speed is 7.6kph higher if there are trees less than 10 meters from the road than if they are 10-30 meters away. This is quite surprising. The Pearson coefficient is 𝑟 = −.385 which is a moderate correlation, and it is statistically significant.

3.2 M

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When examining whether mountains present along the road or not affects the speed, table 5 shows that the Pearson coefficient is 𝑟 = .071, which is quite low. As for trees, the correlation is too small to reflect any trends, even though it is statistic significant. However, combinations of variables for mountains may show some trends. Table 5 presents sceneries of mountains along the road that had some correlations with the speed data. There were no situations with mountains 30-100 meters from the road.

Table 5: Compare means and correlation between the speed data variable and variables of mountains in different combinations

Not present 79.9 855868 9.0 Pearson Correlation .071**

present 81.7 152074 8.8 Sig. (2-tailed) .000

**. Correlation is significant at the 0.01 level (2-tailed).

The only variable in this category that had some correlation with the speed data was the

“Height”. As table 5 shows, the Pearson coefficient is still low 𝑟 = .138 but may give an indication. Due to the lack of observations with mountains more than 10 meter away from the road, only sceneries with mountains or big rocks closer than 10 meters are analysed. The

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amount of mountain or big rocks are not considered because there was no correlation between the amount and the speed data. The average speed is almost 3kph lower in situations with low mountains versus situations with high mountains. This result was not expected.

When looking at registered speed that are higher than 110kph for the same combination of variables, table 5 shows that the average speed is in this case 2.2kph higher in sceneries with low mountains. The Pearson coefficient is 𝑟 = −.120 which is still quite low, but the distribution of registrations is almost the same as for the compared means analysis done with every registered speed included. This gives some indications that those who choose to drive far beyond the speed limit, drive even faster in situations with low mountains than in situations with high mountains (given the mountains are less than 10 meters from the road).

3.3 B

In table 6, it is shown some correlation between the speed data and the variable “Built elements present or not”, which indicates if there are built elements along the road side or not. As the table presents, there is some difference in average speed, where the drivers tend to drive faster if there are no built elements present along the road side.

Table 6: Compare means and correlation between the speed data variable and variables of built elements in different combinations

Built elements Presence Mean (kph)

Built elements Height Mean (kph)

**. Correlation is significant at the 0.01 level (2-tailed).

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One scenery where built elements had some correlation with the speed data, was when the elements were less than 10 meters from the road and they there were either low or high. Table 6 shows that when it was low built elements along the road, the drivers tend to drive faster (2.9kph) than if it was high built elements. Again, the correlation is quite weak with Pearson coefficient 𝑟 = −.131.

The amount (%) of built elements had some correlation with the speed data, and the average speed tends to decrease with higher amount of built elements along the road side. In table 6, only registrations where built elements are closer than 10 meters from the road are included. If built elements were 10-30 meters from the road, it did not influence the speed data. However, the amount is given in how many percent of the image that built elements occupies. That means it also depends on how far away the elements are in the driving direction and how close from the road side.

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