1 Introduction 
Fast driving has a significant impact on the safety of the driver and its environment, as it not only worsens the injuries of those involved in the accident, but also increases the likelihood of having an accident (Elvik, 2005). Faster driving leads to longer braking distances which reduces the drivers response time window on sudden changes and makes driving more demanding, leading to an increased rate of crashes. Furthermore, the faster the car is, the greater the impact of the car crash resulting in more severe injuries. Observations on dutch drivers showed that speeding still has a high prevalence, where around 50% of people exceeded the speed limit when the limit was 80 km/h or slower. With a required speed of 100 km/h around 38% of people still speeded (SWOV Institute for Road Safety Research, 2021). But what leads people to speed while driving? The National Highway Traffic Safety Administration says that this can be split into personal influences and environmental triggers. Personal triggers could be social pressure caused by other drivers or the issue of running late. It is being said that the emotions of the driver is highly being effected by other drivers behaviuor and the traffic situation (Venkatraman, Richard, Magee, & Johnson, 2021). Furthermore, (SWOV Institute for Road Safety Research, 2021) reported, that one of the most common reasons for speeding was adaptation (depending on the road type, 23% to 33% of people listed adaptation as a reason to speed), meaning the speeding was caused by a changed perception of the appropriate speed. Researchers are looking for ways to naturally reduce the speed on the streets and one way is to adapt the design of the streets and therefore influence the environmental triggers. In this paper, we want cite to investigate how intuitive road design might be used to reduce the speed of drivers. More concretely, we want to measure if the existence of road side trees and the variation of road width both have an impact on the perceived speed. The rational is, if someone would consider the speed to be too fast, they would naturally reduce it and therefore drive more safely. Thus, the measured (dependent) variable is a binary question, where the participant indicates if the current speed is "too fast" or "not too fast". The test environment is a survey, in which videos with different scenarios (for different variables values) are shown. 
In this paper, we are mainly investigating two hypotheses. Our first hypothesis is that road side trees will increase the perceived speed. This should lead to more "too fast" responses in the conducted experiment. The reasoning for this is fairly simple, objects that are close to us appear to be moving relatively faster than objects far away. Trees might therefore allow us to perceive the speed more closely to what it actually is. Furthermore, related research also identified, that trees are well suited as speed reference objects, as the complexity of the tree itself is not too high, meaning a driver would not be distracted by them (Naderi, Kweon, & Maghelal, 2008). 
Secondly, we hypothesise that a more narrow road will lead to an increased perception of too fast driving. Smaller roads make the vehicle operators 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. On the other side it will increase their speed and they feel more comfortable driving. This can cause a lack of concentration which increases the chance of a crash and as the car is going faster, 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 following sections we will first describe how exactly we conducted our experiment with the two varying attributes of road width and tree density. Next we will present our findings in the results section, followed by their interpretation and relation to other research in the discussion. 
2 Methods 
The experiment was conducted with 14 participants, 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 participants had to watch videos that showed cars driving 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(European Road Safety Observatory, 2022) . 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 every 10 meters). The values for road width were based on common design choices of European countries, which were cite also suggested in some studies on road design. The scenarios were created 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 OpenSesame 4.0 "Melodra- matic 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 individual. 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 response 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 width, 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 table 1 shows the out- put of LMM model. Similarly to LMM for Response variable, the only random variable is the subject, while the fixed variables are tree density, road with, sex and driver’s license. 
In table 1, 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 influence 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 3 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 significant impact. 
50 km/h 
In case of 50 km/h there start to arise some differences 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 significant 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 
The experiment shows that trees have a significant impact on the speeding behaviour of a driver. Only at the 30 km/h there is no significant impact of the trees visible. This might be the case as 30 km/h is fairly slow for a rural street, no matter how many trees are on the side. It might have a different outcome if the environment would look more like a city environment, since drivers have to pay more attention to the surroundings (cyclists, pedestrians, traffic lights, etc). But to intuitively reduce the speed on these streets maybe other criteria should also be investigated such as road width, curves or speed bumps with a in town environment. 
Interesting to see is that the impact changes when it comes to 50 km/h. 50 km/h is used as a speeding limit for various inner town streets. In this experiment only a rural street environment has been considered. It may be helpful to do the same experiment with inner town street environment in order to find out if this result is also applicable on those streets. If that is the case the city planners should definitely consider planting more trees next to the street. What is left out in this experiment is the impact that the trees have on the quality of the streets. The roots might cause bumps on the road which leads to high re- pairing costs for the city. This was already investigated by the TechBullion which says that the road damage can be avoided by choosing the right trees and planting them deep enough so that the roots do not grow towards the surface.(S, 2023) 
Also for 100 km/h a significant impact is seen. This means that the drivers are confident with driving 100 km/h on this street without trees on the side, whereas with the street trees they are more likely to drive slower. This impact proofs our hypotheses and helps for further investigations. As this experiment has a rather low participant rate and always the same street shown, i would be helpful to do further experiments with more participants and different rural areas. This would give the hypothesis more statistical power and could be used to falsify the theory. 
When looking at the width our hypothesis has not been proven right. As the videos are not able to fully reflect a 3D simulation, the width might have not been recognised that differently. Also no other cars passed by which also makes it hard for the participant to recognise a difference in the road width. To either fully proof this hypothesis wrong or to confirm it, it would be helpful to use a simulator or 3D VR glasses and re-do the experiment with also passing cars. Even though it has not been an significant overall impact, it has had an significant impact on the 50 km/h trials. Therefore it would be even more helpful to re-do the experiment with better technology to either confirm or reject the hypothesis as there are contradictory results in this experiment. 
In general, this experiment has a few biases that should be mentioned within this paper as well. The number of the participant is relatively low which might lead to biased outcomes and can differ compared to experiments with much higher amount of participants. Also only students have been involved which therefore just shows the speed confidence among younger people. Older people for example could be more confident in driving and therefore more likely to speed. It could also be the other way around, that older people are more experienced and therefore are better aware of the dangerous of driving fast and are comparably less risk-taking. Either way, the 
age might have an impact on the driving behavior which has not been considered in this experiment. Additionally the participants only saw videos which do not reflect real world scenarios. A different behavior can be expected when the same participant is being tested on a real world in a real car. 
As in the introduction mentioned the reasons for speeding is split into two major categories: personal circumstances and environmental triggers. This experiment only measures that there is an impact of side trees on the speed but it cannot be clarified why this leads to a reduction in speed. Possible solutions for that could either be the density of an object which leads to better speed transparency and thus the perception of the driver changes or that the trees have an calming effect. It could also be a mixture of both, trees would than not only be beneficial in reducing environmental triggers but also have an impact on the emotions of a driver and there- fore on the personal circumstances. As stated in a paper, trees have an calming effect on people and can reduce stress. It would be helpful to test the effect of street trees on different stress lev- els of participants to see the impact of trees not only environmentally but also emotionally. This could be done by showing the same street, the same environment and the same speed with different stressing distractors while measuring the stress level. Once done with trees and once done without trees. 
So overall the experiment was a good way to see if trees or width in general could have an impact on the speeding behaviour. But to gain high statistical power and make this theory re- liable, further investigation in this area in different ways should be considered. As mentioned above, people ́s behaviour can differ when just see- ing a video, thus experiments within real world scenarios should be considered to fully proof the hypothesis. 

Based on this scientific research paper write a brief 200 words abstract for a cognitive psychology scientific research paper