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Several aspects contribute to road crashes and one important part is the ‘human factor’. This information is interesting but insufficient unless we also try to understand what is meant by the term. Three different features have been defined: errors, lapses and violations and the latter, which is a deliberate act, has been found to be the main contributor to road crashes. The crucial issue is therefore to understand what motivates drivers to commit an act, which puts both themselves and others at risk. The aim of this thesis is to explore the motives behind this behaviour through the use of an extended version of the theory of planned behaviour (TPB). Four different studies were carried out: The first study is qualitative, investigating the intention to violate. The second one assesses speeding in an urban area and dangerous overtaking. In addition to variables within the model, descriptive norms and past behaviour are included. The third study explores what particular beliefs are responsible for the behaviour. The fourth study uses the TPB to predict intention to speed on a rural road and assesses some underlying factors, such as ambivalence and gender. The results of the thesis show that the theory explains 33 to 53% of the variance in intention to violate and that descriptive norm and past behaviour significantly increase the explained variance. Descriptive norm is also related to risk and past behaviour is not only related to intention but also to the variables within the model. The results show that drivers’ beliefs can distinguish between intenders and non-intenders. With regard to attitudes the general conclusion is that the main difference lay in the effect of positive outcomes. Although in a more ‘risky’ situation the behaviour is more controlled by a denial of negative consequences. Finally, the results indicates that in the context of driving violations an expressed low level of control over the behaviour could be interpreted as a form of denial of responsibility rather than an inability to control their own actions. Implications of the current findings for the development of intervention programmes are discussed.

The car has become bigger, faster and more advanced for each passing year since its first appearance, and the safety requirements have also become stricter. Computer vision based support is a growing area of safety features where the car is equipped with a mono- or stereo camera. It can be used for detecting pedestrians walking out in the street, give a warning for wild-life during a cold January night using night-vision cameras and much more. This master thesis investigates the problem of detecting and tracking overtaking vehicles. Vehicles that overtake are only partly visible in the beginning, rendering it hard for standard detection/classification algorithms to get a positive detection. The need to quickly detect an incoming vehicle is crucial to be able to take fast counter-measure, such as braking, if needed. A novel approach referred to as the \textit{Wall detector} is suggested, detecting incoming vehicles using one-dimensional optical flow. Under the assumption that an overtaking car is moving in parallel to the ego-vehicle, both cars are moving towards the vanishing point in the image. A detection wall, consisting of several detection lines moving towards the vanishing point, is created, making all objects that are moving parallel to the ego-vehicle move along these lines. The result is a light-weight and fast detector with good detection performance in real-time. Several approaches for the Wall detector are implemented and evaluated, revealing that a feature based approach is the best choice. The information from the system can be used as input to heavier algorithms, boosting the confidence or to initialize a track.

<p>In this thesis work I have been working with two traffic simulators called Hank and ST Software. Hank is a research tool at the University of Iowa and ST Software is a commercial product. To evaluate which of these is the most suitable for behavior research I have implemented three types of intelligent agents: Overtaking Agent, Traffic Light Agent and Meeting Agent. The thesis work was extended by adding the possibility for realistic human behavior to the agents.</p><p>The result indicated that Hank allowed for greater control over behavior while ST Software allowed for faster and easier implementation.</p>

Models based on micro-simulation of traffic flows have proven to be useful tools in the study of various traffic systems. Today, there is a wealth of traffic microsimulation models developed for freeway and urban street networks. The road mileage is however in many countries dominated by rural highways. Hence, there is a need for rural road traffic simulation models capable of assessing the performance of such road environments. This thesis introduces a versatile traffic micro-simulation model for the rural roads of today and of the future. The developed model system considers all common types of rural roads including effects of intersections and roundabouts on the main road traffic. The model is calibrated and validated through a simulation study comparing a two-lane highway to rural road designs with separated oncoming traffic lanes. A good general agreement between the simulation results and the field data is established. The interest in road safety and the environmental impact of traffic is growing. Recent research has indicated that traffic simulation can be of use in these areas as well as in traditional capacity and level-of-service studies. In the road safety area more attention is turning towards active safety improving countermeasures designed to improve road safety by reducing the number of driver errors and the accident risks. One important example is Advanced Driver Assistance Systems (ADAS). The potential to use traffic simulation to evaluate the road safety effects of ADAS is investigated in the last part of this thesis. A car-following model for simulation of traffic including ADAS-equipped vehicles is proposed and the developed simulation framework is used to study important properties of a traffic simulation model to be used for safety evaluation of ADAS. Driver behavior for ADAS-equipped vehicles has usually not been considered in simulation studies including ADAS-equipped vehicles. The work in this thesis does however indicate that modeling of the behavior of drivers in ADAS-equipped vehicles is essential for reliable conclusions on the road safety effects of ADAS. / <p>Report code: LiU-Tek-Lic-2005:60.</p>

Driving simulators and microscopic traffic simulation are important tools for making evaluations of driving and traffic. A driving simulator is de-signed to imitate real driving and is used to conduct experiments on driver behavior. Traffic simulation is commonly used to evaluate the quality of service of different infrastructure designs. This thesis considers a different application of traffic simulation, namely the simulation of surrounding vehicles in driving simulators. The surrounding traffic is one of several factors that influence a driver's mental load and ability to drive a vehicle. The representation of the surrounding vehicles in a driving simulator plays an important role in the striving to create an illusion of real driving. If the illusion of real driving is not good enough, there is an risk that drivers will behave differently than in real world driving, implying that the results and conclusions reached from simulations may not be transferable to real driving. This thesis has two main objectives. The first objective is to develop a model for generating and simulating autonomous surrounding vehicles in a driving simulator. The approach used by the model developed is to only simulate the closest area of the driving simulator vehicle. This area is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to a microscopic model which includes sub-models for driving behavior, while vehicles in the outer regions are updated according to a less time-consuming mesoscopic model. The second objective is to develop an algorithm for combining autonomous vehicles and controlled events. Driving simulators are often used to study situations that rarely occur in the real traffic system. In order to create the same situations for each subject, the behavior of the surrounding vehicles has traditionally been strictly controlled. This often leads to less realistic surrounding traffic. The algorithm developed makes it possible to use autonomous traffic between the predefined controlled situations, and thereby get both realistic traffc and controlled events. The model and the algorithm developed have been implemented and tested in the VTI driving simulator with promising results.

Driving simulators are used to conduct experiments on for example driver behavior, road design, and vehicle characteristics. The results of the experiments often depend on the traffic conditions. One example is the evaluation of cellular phones and how they affect driving behavior. It is clear that the ability to use phones when driving depends on traffic intensity and composition, and that realistic experiments in driving simulators therefore has to include surrounding traffic. This thesis describes a model that generates and simulates surrounding vehicles for a driving simulator. The proposed model generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between vehicles. The model is built on established techniques for time-driven microscopic simulation of traffic and uses an approach of only simulating the closest neighborhood of the driving simulator vehicle. In our model this closest neighborhood is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to advanced behavioral models while vehicles in the outer regions are updated according to a less time-consuming model. The presented work includes a new framework for generating and simulating vehicles within a moving area. It also includes the development of enhanced models for car-following and overtaking and a simple mesoscopic traffic model. The developed model has been integrated and tested within the VTI Driving simulator III. A driving simulator experiment has been performed in order to check if the participants observe the behavior of the simulated vehicles as realistic or not. The results were promising but they also indicated that enhancements could be made. The model has also been validated on the number of vehicles that catches up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways.

Le principal objectif de ce travail de thèse est de valider l’hypothèse générale selon laquelle les conducteurs automobiles sélectionnent un mode d’action et le régulent en percevant des possibilités de dépassement, appelées affordances. À travers trois études réalisées sur simulateur de conduite, nous avons montré tout d’abord que les conducteurs prennent la décision d’initier un dépassement en fonction d’une affordance définie par le ratio entre les propriétés du système agent-environnement (la vitesse minimale satisfaisante pour réussir un dépassement (MSV)) et la propriété de l’agent (vitesse maximale du véhicule conduit (Vmax)). Nous avons ensuite mis en évidence que la décision d’initier un dépassement dépendait majoritairement de l’affordance de haut niveau MSA/Amax, définie par le ratio entre une propriété du système agent-environnement (accélération minimale satisfaisante pour réussir un dépassement (MSA)) et la propriété de l’agent (accélération maximale du véhicule conduit (Amax)). Enfin, nous avons démontré la capacité des conducteurs à exploiter des possibilités de dépassement définies par le ratio entre les propriétés du système agent-environnement (MSA) et les propriétés de l’agent (accélération maximale du véhicule conduit en 4ème (Amax 4ème) et accélération maximale du véhicule conduit en 3ème (Amax 3ème)). La formalisation de ces affordances nous a ainsi permis d’élargir le domaine d’application de l’affordance-based control (Fajen, 2007a) à des tâches de dépassement très proches de situations réelles. De futures recherches permettront d’identifier les invariants optiques sous-jacents au dépassement et d’étudier les modalités de leur prise en compte. / The main objective of this thesis is to validate the general hypothesis that car drivers select a mode of action and regulate it by perceiving overtaking possibilities called affordances. Through three experiments carried out on driving simulator, we firstly showed that car drivers take the decision to initiate an overtaking on the basis of an affordance defined by the ratio between the properties of the agent-environment system (minimum satisfactory velocity to succeed an overtaking (MSV)) and the agent property (maximum speed of the vehicle driven (Vmax)). Secondly, we have highlighted that the decision to initiate an overtaking depended mainly on a high order affordance, the MSA/Amax, defined by the ratio between a property of the agent-environment system (minimum satisfactory acceleration to succeed an overtaking (MSA)) and the property of the agent (maximum acceleration of the vehicle driven (Amax)). Thirdly, we showed that drivers are able to exploit the overtaking opportunities defined by the ratio between the properties of the agent-environment system (MSA) and the agent’s properties (maximum acceleration of the vehicle driven in 4th gear (Amax 4th) and maximum acceleration of the vehicle driven in 3rd gear (Amax 3rd)). The formalization of these possibilities for action allowed us to expand the scope of the affordance-based control (Fajen, 2007a) to the overtaking tasks which are close of situations encountered in daily life and to take a new look at this kind of tasks. Future research will be needed to identify the optical invariant underlying the overtaking and to study how they are taken into account, especially when a competition between affordances occurs.

In this thesis work I have been working with two traffic simulators called Hank and ST Software. Hank is a research tool at the University of Iowa and ST Software is a commercial product. To evaluate which of these is the most suitable for behavior research I have implemented three types of intelligent agents: Overtaking Agent, Traffic Light Agent and Meeting Agent. The thesis work was extended by adding the possibility for realistic human behavior to the agents. The result indicated that Hank allowed for greater control over behavior while ST Software allowed for faster and easier implementation.

The master’s thesis deals with upgrading of the Dluhonice railway station. The aim is design the station to suit the operation and the requirements of the manager. The integral parts of the thesis are adjustments to the railway superstructure, substructure and drainage system of the station.