1 Introduction 
Fast driving has an significant impact on the safety of the driver and its environment. Faster cars have a longer braking distance which reduces the drivers response time on sudden changes and can cause crashes. Furthermore the faster the car is, the greater the impact of the car crash and bigger injuries can result. Speeding has become more acceptable over the past years and studies in 2019 show that people tend to speed more com- pared to the previous years. (Speed and speed management (swov.nl)). Researchers are looking for ways to naturally reduce the speed on the streets and one way is to have a look on the design of the streets. In this paper the intuitive driving takes the components the driver and the design of the road into consideration in order to find out how the road design can help the driver to find the optimal speed in an intuitive way. The 
major reason why intuitive speed limits are im- portant to discuss is that people are not getting used to that and will not drive faster once they feel more comfortable (How to slow down traf- fic: ditch kerbs, keep potholes, plant trees | Road safety | The Guardian) It is being said that street trees have a positive impact on reducing the fre- quency and severity of crashes ((PDF) The street tree effect and driver safety (researchgate.net)). Not stated in that article is whether this is caused by the calming function of a tree on the mind or if the speed is being reduced on an intuitive way. Our hypotheses is that trees in general reduce the speed in comparison to streets without trees. We believe that the speed is being reduced by street trees as the driver gets a better visibility of how fast the car is driving while passing close and rel- atively big objects on the side of the road. The further the objects are away, the longer they are visible to the driver so that the feeling of driving fast is lower than if he passes several different objects in the same time. In the article it is also stated that trees are good side street objects as they have a good mixture of being simple and not too boring. So the drivers do recognize the trees but are not distracted by them. A second hypothesis is that the width of the street has an impact on the speeding. Smaller roads make the driver drive more carefully and the speed is being reduced. People tend to believe that wider roads make driving more safe. The wider the road is the more manoeuvring space the drivers have but on the other side it will increase their speed and they feel more comfortable driving and a lack of concentration might cause a crash and as the car is driving fast it will be a more dangerous crash compared to lower speeds. (Bigger Isn’t Always Better: Narrow Traffic Lanes Make Cities Safer | World Resources Institute (wri.org)) In the fol- lowing paper two attributes of the road design will be tested. One, how the street trees have an impact on the favourable driving speed and sec- ond, if the width of the road affects the speed of the driver. 
2 Methods 
The experiment was conducted with 14 partic- ipants, of which 4 were female and 10 male. The age range in the population was 21 to 35 years. Participants were recruited on campus and all of them were students. Each of the partici- pants had to watch videos that showed cars driv- ing through a specific environment from a first person view, where a video showed the same overall environment with varied parameters. The environment for the scenario was a rural two lane street with some distant objects like cities or trees during daytime and without junctions. This environment was chosen as a recent report from 2021 on road fatalities in the EU showed that most fatal crashes happened on rural roads, without junctions, during the day with personal transport vehicles(Directorate-General for Mo- bility and Transport, ). Speed, tree density at the sides of the road, and the road width were the altered parameters. The choices for speed were either 30, 50 or 100km/h, for road width 2.8 or 3.5 meters and for tree density none (no trees), sparse (trees every 20 meters) or dense (trees ev- ery 10 meters). The values for road width were based on common design choices of European countries, which were also suggested in some cite studies on road design. The scenarios were cre- cite ated using Blender 2.9.1 and exported to Assetto Corsa, a racing simulator that was used for the actual simulation of the car driving in the scene. 
After the simulation videos were recorded, they were integrated into an experiment using OpenS- esame 4.0 "Melodramatic Milgram". An example of how a scenario looked can be seen in Figure 1. 
In the experiment, age, sex and the ownership of a drivers license were recorded for every in- dividual. After confirming to a consent form, 18 videos with unique combinations of parameters were shown in random order, where for each video the participant had to make a binary decision, if the shown speed given the current scenario was too fast nor not too fast. A video was presented for a maximum of 16 seconds during which the participant had to give an answer. If either time ran out or a decision was made by the subject, the video was stopped and a focus point was shown for 500 milliseconds before the next video was played. The response together with the re- sponse time were recorded for each of the deci- sions made. 
 3 Results 3.1 Response 
Let us consider the output of fitted LMM model in the figure 2. The only random variable is the subject, while the fixed variables are tree density, road with, sex and driver’s license. 
It can be observed that p-value for tree density dense is 0.13 > 0.05, which does not reject the null hypothesis. However, in case of tree density sparse the p-value is 0.009 < 0.05 which allows to confidently reject the null hypothesis, and claim that sparse tree density has a significant in- put on speed perception. 
When it comes to road width, the p-value is equal to 0.20 > 0.05 for wide road versus narrow road. This does not allow to reject the null hypothesis. 
3.2 Response Time 
As for Response Time, the figure 3 shows the out- put of LMM model. Similarly to LMM for Re- sponse variable, the only random variable is the subject, while the fixed variables are tree density, road with, sex and driver’s license. 
In the figure 3, looking at t-values of tree densities it can be seen that both dense and sparse features have t − value < |1.96|, especially sparse with t − value = −0.185. It suggests that both tree densities have significant influ- ence on Response Time when perceiving speed. The negative sign of t-value suggests that subjects were more confident and the response time was shorter in the presence of trees on the road. 
Similarly road width had a significant influence on Response Time, with t-value = −0.520 for wide roads. Again, the negative t-value suggests that subjects were more confident in their response when showed a wider road. 
3.3 Multiple features setup 
Figure 4 shows perception of speed with within subjects when confronted with multiple factors present at the same time. 
30 km/h 
In that column there can be seen no significant differences when perceiving speed. It might be the case that 30 km/h is simply too slow for the tree presence or road width to have any signifi- cant impact. 
50 km/h 
In case of 50 km/h there start to arise some dif- ferences in perception of speed for multiple se- tups. In case of narrow road with dense trees the biggest number of participants responded with "too fast", while wide road with dense and sparse trees scored the lowest number. It can bee seen that road wideness has significant impact on perception of speed when driving 50 km/h. 
100 km/h 

When it comes to 100 km/h, it is quite clear that 
presence of trees starts to have a signifi- cant impact as well. With the lack of trees, the lowest number of subjects responded with "too fast", while their presence increases the sense of speed. It might be the case the wideness of the road has smaller impact in that scenario. 
4 Discussion 

Given the above research paper for a cognitive psychology project, please provide me a brief 200 words summary