Introduction

Previous studies demonstrate that high speed is one of the most important contributors of fatal accidents in Norway. Strong statistical relationships between road safety and speed have been established (Elvik et al., 2004), where lower speeds results in fewer number of accidents and injured road users. Generally, speed limits are meant to give the driver information about the speed they can drive safely under normal conditions. This does not necessarily result in drivers adhering to the recommended speed limits. Driving faster than the speed limit is very common at all road types (Goldenbeld & van Schagen, 2007).

In order to develop effective road safety measures, it is important to understand what influences the driver’s behavior. What is the reason for driver’s choice of speed? It is well-known that age, gender, road design, speed limit and weather conditions affect how people are driving (Rhodes and Pivik, 2011; Morgan and Mannering, 2011; Bener, 2013; Hjelkrem and Ryeng, 2016). In addition, there are a number of other factors that may influence the driver’s speed choice. One of these factors may be the road’s visual surroundings, which has been demonstrated already by previous research. We define visual surroundings as what the driver sees that is not a part of the actual road, such as vegetation, mountains/big rocks, built elements and lampposts that are close to the road. This paper presents a study investigating whether and how the visual surroundings on Norwegian rural roads contributes to a driver’s speed choice.

1.1 E

This literature review presents theories that may explain how drivers choose their speed and what methods previous studies have used to examine the effects of visual surroundings.

Drivers continuously calculate the extent of risk they feel exposed to (Wilde, 1994), and then compares it with the amount of risk they are willing to take. Hence, risk may be an important factor in the drivers’ behavior on the road. A central element in Wilde’s (1982) theory of risk homeostasis is called a comparator. The term comparator implies that actions are triggered by internal scenarios where something is compared consciously. The comparator could be viewed as a focal point where the effect of all variables meets and are considered in terms of risk, before the driver’s speed and behavior are decided. This will be the place where “the effects of all influencing conditions are collected and constitutes a final instance before action” (Vaa and Bjørnskau, 2002, p.29).

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Vaa and Bjørnskau (2002) claims that the human is equipped with something they call a risk monitor. The risk monitor is supposed to ensure survival and it is based on primary and secondary emotions (innate and experience-based emotions), but also feelings. The driver’s behavior is based on earlier experiences such as being in danger, out of control or some other form of discomfort, both emotionally and physically. The risk monitor is based on experience-based emotions, therefore familiar surroundings will feel safer. Vaa and Bjørnskau draws a direct link to driver behavior, where driving on a new and unknown road requires increased preparedness in the risk monitor. Vaa and Bjørnskau describes this in an example where it was shown that accidents on slippery roads increased after the introduction of mandatory “skid control course” in the 80’s. In this case, the risk monitor was “manipulated” where the driver got the feeling of mastering slippery roads.

The literature from Vaa and Bjørnskau (2002) gives an understanding of how driver’s behavior and speed choices are decided. This can be used when examining how the visual surroundings contributes to a driver’s speed choice. The problem may be the great variation between drivers.

That is, they have different range of tolerance. Some may tolerate a large number of variations of visual or other factors in traffic without affecting the speed choice. The risk monitor may be individual and cannot necessarily be representative for different groups or locations.

Aarts, Brandenburg and van Nes (2011) claims that the density of the environment along the road (like trees and shrubs), affects the choice of speed. They tried to find primary and secondary credibility factors to investigate the relation between speed and the environment. As speed data, they used the 2007 annual speed data per road section. A total of 40 single lane rural road sections in Netherlands were analysed using pictures from the sections. Higher density led to lower speed. There was no difference in speed based on the width of the road or the

“straightness” of the road, something Vaa and Bjørnskau (2002) assumed would influence the choice of speed. Aarts, Brandenburg and van Nes refers to Goldenbeld and Van Schagen (2007) explaining that the tendency to exceed the speed limit is greater when the speed limit lacks credibility in terms of characteristics and surroundings.

Kosztolanyi-Ivan, Koren and Borsos (2016) looked at situations where it is difficult to distinguish between built-up and non-built-up areas. They handed out pictures (without saying what type of area it was) from non-built-up, built-up and transition areas to 500 participants and asked them what speed they would have chosen. The results from their study showed that the speed’s standard deviation was bigger in the transition areas than in a clearly built-up or non-built-up area. This demonstrates that drivers have difficulties estimating the adequate speed for

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mixed environments. Kosztolanyi-Ivan, Koren and Borsos also developed a computer program that was supposed to recognize built-up and non-built-up areas. This proved to be difficult as the program did a lot of mistakes, especially in transition or unclear areas. According to Kosztolanyi-Ivan, Koren and Borsos, classification of such situations had a reasonable match between humans and the computer.

Prototypical presentations of traffic surroundings are the basis for the categorisation process and contain information about typical spatial relationships between road elements and road users. Abstract representations of the world are stored in the memory (Theeuwes and Godthelp, 1995). Such prototypical representations are developed through experience. To ensure unity on how people structure the world, it is necessary that there is consistency in the environment’s or object’s physical appearance, as well as consistency in terms of behavior in relation to the object or environment. Self-explaining roads must be in line with the road user’s expectations (Theeuwes and Godthelp). Roads must be homogenous within the road category (main roads, highways etc.), but still different from all other road categories. That way, Theeuwes and Godthelp assumes that the drivers can learn to distinguish between the different road categories and better understand situations. This contradicts to the article of Kosztolanyi-Ivan, Koren and Borsos (2016), claiming that it’s difficult for people and computers to classify roads. Berge (1992) also claims that road users don’t always understand the reason for a given speed limit due to the surroundings.

Naderi, Kweon, and Maghelal (2008) examined how trees affect safety and choice of speed.

Using a 3D simulator (Drive Safe), they tested the isolated effect of having trees or not, close to the road. The results show that participants chose a lower speed if there were trees along the roadside, regardless of contextual environment.

The results from an experiment by Antonsen et al. (2009) showed that there was a significant difference in average speed for different landscape types (open, forest, varying landscape), with the lowest average speed found for the varied landscapes and highest average speed for the open landscape. Antonsen et al. suggests this may be due to a context of shorter distance of sight and heterogeneity in the environment. In this experiment, they used a Swedish driving simulator (Driving Simulator III, at VTI). Antonsen et al. explains that previous studies that examine how the landscape affects the driver’s behavior have several weaknesses. Photos and videos of someone driving through a landscape can be differently perceived compared to real life driving. As a driver, you must consider the speed and oncoming traffic, which means that the driver’s focus on landscape is limited. Some driving simulators provide the feeling of forces

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like for example braking, but not the feeling of contact between the road and the car. Antonsen et al. believes that the best way to examine how the landscape affects driver’s behavior is to use real situations where the car actually drives through real landscapes.

Precipitation, lighting and surface conditions can also affect the driver’s behavior. Hjelkrem and Ryeng (2016) have studied some of the underlying mechanisms of driver behavior in adverse conditions. For rural roads where two consecutive vehicles are in interaction, they considered how rainfall, lighting conditions and surface conditions can affect drivers’ risk perception by defining an indicator (CRI- Chosen Risk Index). CRI describes the chosen risk level for drivers in a situation of two consecutive vehicles. In this study, CRI is defined as speed multiplied by weight, divided by the time gap. Low CRI leads to high perceived risk. For cars, Hjelkrem and Ryeng found that a snowy road with moderate rain or light snow gives the highest perceived risk (lowest CRI). For trucks (3500kg+) the CRI dropped significantly when the road was partially or completely covered with snow, while at night or during dusk the CRI would increase. The study shows that rainfall, lighting and surface conditions affect the driver’s behavior, and argues that this is due to the driver’s perception of risk.

1.2 S

The purpose of this study is to explore the association between a driver’s speed choice and visual surroundings along the road, such as trees, built elements and mountains/big rocks. While previous studies basis their speed data using driving simulators or images with surveys, this study uses loop detectors from actual roads in Norway to collect speed data. Speed data from 23 loop detectors from a selection of rural roads with a speed limit of 80kph are analysed.

Utilizing pictures viewed from the driver’s perspective at each of these loop detector sites, the visual surroundings are investigated. The visual surroundings are described by selected variables. Thereby, a comparison between the surroundings and registered speed data is possible. Based on previous studies, it is expected that the presence of trees, mountains or built elements close to the road will affect the speed choice. When trees are less dense, it is expected a higher average speed, and when the trees are closer to the road it is assumed to be a lower average speed. For built elements and mountains, it is expected lower speed when these are closer to the road or the amount of them are higher.

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