NEO Five Factor Inventory (Neo-FFI)

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Fixed-Based Simulated Environment

This simulated environment gives no feedback from the steering wheel and the chair.
A validity study made by Reed and Green on fixed-based simulator and comparison with real world cars shows that fixed-based simulators has been found to be less precise than that of actual vehicles or moving-based simulators mainly due to the lack of motion queues.
Sideway steering precision was worse in the simulator as compared to a real car. However in Reed and Green study showed that participants were better at keeping a constant speed in the simulator as compared to real car. They claimed that it is most likely a result of the absence of wind and uneven roads.
The degree of importance of the simulator‟s validity can vary depending on the nature of study and ecological validity of the simulator. Advanced and expensive simulators may seem to be the best option but depending on the research question and the goal of study. One may be able to use a simpler simulator with less ecological validity and still get good results. More advance simulators should be used in those cases where the main focus is to get the realistic feeling of driving and that the ecological validity must be high. Whereas if the main focus is on the behaviors in a driving environment, then a less advance simulator might me suffice (Santos et al., 2005).
Reed and Green study shows us that fixed-based simulators are usable tools when it comes to study of human behavior in traffic, and they show a high absolute validity on speed control but shows an overall low absolute validity. At the same time they show a high relative validity on the driving accuracy. A fixed-based simulator has generally low absolute validity and high relative validity.
Santos‟ et al. (2005) study on driving behavior using a simulator compared and evaluated a standardized visual performance test in three different environment‟s laboratory, simulator and instrumented vehicle. The goal of study was to assess the suitability for each test environment for testing the effects of In-Vehicle-Information-System on driving performance.
Simulator results couldn‟t be applied to the real world whereas they gave an enormous strength to the experimental design. A comparison between simulator and the instrumented vehicle with self report data indicated that the level of seriousness, of potential effect with respect to traffic safety, was lower in the simulator compared to the instrumented vehicle. Despite this the study provided a good first insight to the vehicle development industry for the improvement of assessing and designing for example safety systems.
A study made by Hancock and de Ridder (2003) on accident avoidance behavior in a controlled simulated environment, focus on the final seconds and milliseconds before a collision. There simulated environment was constructed so that drivers could be seated in two full vehicle simulators and interact within the same simulated world. That setup meant that they could create simulated situations that could evoke responses paralleling those observed in real world situations. In the experiment, a total of 45 participants were tested within the virtual world. Two ambiguous traffic situations were created for the driving participants, who crossed path with each other in the simulated world. Those situations were an intersection and a hill. In the intersections the two drivers met by approaching each other from an angle of 135 degrees. Objects blocked their views to keep both drivers from detecting each other early in the scenario, where as the hill scenario represented a wrong way conflict. Qualitative results were obtained through post experience questionnaires about the participant driving habits, simulated experience and their response to specific experimental events. Their study shows that situations with realistic avoidance response behavior can be created and replicated in a simulation environment. Hancock and de Ridder (2003) methodology is used in our research.

Factors influencing safety

People‟s behavior and performance is affected by several factors of daily life. In particular case of traffic safety, it is prohibited to drive a car after taking alcohol or other drugs. Jeffery Archer (2005) explains internal and external factors like stress, fatigue, social psychological factors such as attitudes, social cognition, biases and norms and personality factors of daily life which can affect a person‟s behavior during driving. Swedish Road Authority (SRA, 1996) has also identified human functions that are critical for safe driving. SRA points out that stress, strain, tiredness, alcohol, medication as factors which can have a serious negative effect on driver performance. They also identified that inexperienced and incorrect attitudes of drivers can be potential problem areas with regard to safety. The factor‟s which can be important from research point of view and can be studied during simulated research involves drivers attitude and stress about the task and effect of this on the driving parameters.

Effect of Stress on driving behavior

According to Hennessy and Wiesenthal (1999) many traffic accidents occur due to stress or aggression in the driver‟s behavior. The reason of person‟s in stress may be due to the problems of job or home or time pressure. A stress condition during driving can make the person behavior aggressive on the road, as a result can be a cause of accident. Hennessy‟s and Wiesenthal (1999) research examine the difference between the stress and aggression state of driver‟s in high and low congestion conditions. The results of the study shows a high level of stress and aggression in the driver‟s in high congestion conditions. It is noticeable that time factor was main cause of stress in high and low congestion conditions, and aggression was the cause of stressful behavior in high congestion conditions.
Howard and Joint (1994) discuss the relation between stress and fatigue in driving. They say that long distance driving becomes the cause of fatigue for drivers whose minds are busy in thinking something else while they are driving. Those drivers are driving without awareness. This fatigue can cause stress in drivers, which is a result of concentration of mind in other things during driving. It is seen that when you are concentrating on other things during driving, distraction can occur easily. This distraction during the driving can be dangerous and hinders the driver‟s concentration during driving and cause accidents.
This study proves that stressful conditions can affect a persons driving behavior, yet this research is not opted to create stress less behavior.

Variations and traffic velocity

A connection between the variability of the vehicles speed and road safety is discussed by Solomon (1964). There is a deep connection between speed of the vehicles and traffic safety. Speed reduction helps to improve safety on roads. Solomon in his paper discusses relationship between crash risks and variability in speed i.e. sub-optimal interaction in present in traffic streams by different speeds of the vehicles.
Solomon study shows research of 10,000 crash reports in 35 sections of rural highways between 1955 and 1958. He shows that pre-crash speed of vehicle was determined by police, the driver himself or the witnesses of the crash sites. Solomon study involves the calculation of 290,000 vehicles speeds on each of the 35 sections of rural highways. He than calculated the mean speed for each of the 35 sections. While doing the comparison of the speed of the vehicle during the crash with means speed of particular road section Solomon found that crash-involvement rate was higher when vehicle drove faster or slower than the mean speed of the particular section. And the crash-involvement rate was rather low where mean speed is close to the vehicle speed.
The accidents occurring at mean speeds below average could be because of numbers of intersections and driveways on the crashes spots stated by Frith and Patterson (2001). Those points may be the reason for congestion on the roads. Solomon also states the same consideration, with reference to other places which also share the same point that intersections and other access points are reason for large number of accidents. So it is not correct to conclude that only high or low speed on highways can be a factor for accidents occurrence, also low speeds at intersection may also cause accidents to occur.
In the Solomon‟s study results 46% of the totals are low-speed crashed, 51% were rear-end crashes and 38% were angle crashes. The accidents occurring at intersections and congestion conditions at rear-end. Driving with high speed are cause accidents to occur more frequently.
“The smaller the variation within the stream, the smoother and safer the traffic flow will be.” (p. 6) said by Frith and Patterson (2001). A study about New Zealand in Frith and Patterson (2001) tells us that if the mean speed is lowered the variation in the traffic is decreased. This takes us to the conclusion that the reduction in speed variation takes us to the traffic safety. For decreasing the variability in the traffic velocity, we can make the slow moving drivers drive fast or fast moving drivers drive slow. Later case is the best option, as fast driving can lead to more accidents, so it is better to reduce there speed.
This shows that traffic velocity has significant role in the traffic safety. In simulated environment traffic velocity can be enforced easily with the help of simulation experiments, which is the reason of including range of traffic velocity in our experiments.

Violation of expectations

Anticipated behavior of other people is expectation. During communication it can be generalized or person specified. Expectancy violation theory can be used to examine effects of expectations. According to this theory, other people expect the communication behavior from others and violation of their expectations lead to a cognitive evaluative process that results in either a positive or negative evaluation of the perceived outcome. Following are the factors that affect the outcome, according to the expectation violation theory: target characteristics, relationship characteristics and context features.
Theory of expectancy and Theory of expectancy violation provides support to study the behavior changes occur due to the changes in perceived and expected behavior. In order to study changes in behavior, differences in expectations are created by e.g. manipulation of instructions. Expectancy violation theory according to Bonito et al. (1999) “…is concerned with the degree to which expectations frame behavior and the consequences of such framing on interaction and task outcomes” (p. 231). Interpretation and evaluation of violation of expectations is a very important issue of expectancy violation theory. Positive or negative valence is assigned during the characterization of violations. Positive violation are socially valued behavior that exceeds the quality of anticipated actions, while on the other hand negative violations are relatively undesirable acts that fall short of expectations.
In 1976 Burgoon introduced the theory of expectation violation. It was at that time used at theory of non-verbal behavior. This theory is specific towards theory discourse and interaction and also considered as theory of communication processes. Burgoon and Le Poire, (1993) presented their study “Affect of communication expectancies, actual communications, and expectancy disconfirmation on evaluations of communicator and their communication behavior” and Bonito et al. (1999) presented “The role of expectations in human-computer interaction” both uses those theories in their studies. The study by burgoon and Le Poire shows changes in expectations due to different factors. Bonito et al. (1999) study shows support of expectancy violations theory‟s premises and predictions. The study by Bonito et al. (1999) shows the decision making tasks difference when people are interacting with the human partners or compute agents. Influence and perceptions of partners are affected by expectations and evaluations which are shown by correlation analysis of five different computer conditions.
In our experiments, drivers expectation are analyzed by providing them different instruction sets to see if it would create deviations of expectations, so to view a change in the driving behavior of the drivers.

Conflict Indicating Variables

A traffic conflict may result in a collision if the traffic participant would have continued with the same speed and direction. It is always desired to be able to predict the probable accident or collision by finding different factors involving the collision. Conflict indicator variables are set of those factors which can be used to identify the circumstances where an accident is about to be happen. For the analysis work, these variables have lot of importance in order to draw useful outcomes. It is possible to get these details from the simulated environment with respect to the each participant and analyze his behavior. Conflict indicator‟s classification depends on its handling in a specific safety situation.
Conflict variables can be calculated using two types of method.
 Objective Methods
These methods feature time distance and speed to calculate the severity of the safety situation.
 Subjective Methods
These methods are dependent on human observation, who records the perceived risk at the moment of conflict. It is also possible to use a video observation from a fixed camera
To be able to get better result the combination of the mentioned methods may be used (Lu et al., 2001). The merging of these methods results an appropriate risk value.
The usefulness of these variables can be classified by evaluating the following three criteria (Svensson 1998):
1. Indicators should complement accident data and be more frequent than accidents
2. Indicators have a statistical and causal relationship to accidents
3. Indicators have the characteristics of ‘near-accidents’ in a hierarchical scale that describes all severity levels of driver interaction with accidents at the highest
level and very safe passages with a minimum of interaction at the lowest level.
Some of the variables which are relevant for the current study are described as under.

Time to Collision (TTC)

In studies related to Traffic Conflicts Techniques, Time-To-Collision (TTC) has been considered to be a valuable measure for rating of traffic conflicts‟ severity and to differentiate between critical and normal behavior.
Hayward (1972) defined TTC as: « The time required for two vehicles to collide if they continue at their present speed and on the same path ». TTC continue to be decrease if there is no change in speed and path. Disadvantages of TTC according to Archer are that it does not clarify the sternness of a traffic situation and not a good measure for comparison.

Extended Time to Collision (TET, TIT)

Two alternative to proximal safety indicators have been proposed by Dutch researchers Minderhood and Bovy (2001) on the basis of general principles of the Time-to-Collision concept. These are Time Exposed TTC (TET) and Time Integrated TTC (TIT).For the period where TTC-event remains a chosen TTC-threshold TET is used to calculate the time of that period. On the other hand TIT referred to as Time Integrated TTC (TIT), is similar to the TET but it represents a measure of the integral of the TTC-profile during the time it is below the threshold.
Figure 3: The Time Exposed and Time Integrated TTC proximal safety indicator measures proposed by Minderhoud and Bovy (2001)

Time to Accident (TA)

Time to Accident is a Safety indicator measure which based on a subjective estimation of speed and distance for conflicting road-users at a common conflict point. The Time-to-Accident measure is recorded only once at the time when evasive action is first taken by a conflicting road-user. TA-values are used in determination of the scale of conflict seriousness accordance with a threshold function (Archer 2005).

Post-Encroachment Time (PET)

Post-Encroachment Time (PET) is the further variation of TTC. PET is used to measure in situations where two road-users, not on a collision course, pass over a common spatial point or area with a temporal difference that is below a predetermined threshold (Archer 2005) (typically 1 to 1.5 seconds).
The main difference between PETs and TTCs is the absence of the collision course criterion i.e. even if no collision occur PET value can be calculated. PET‟s can be easily extracted using photometric analysis, video or simulated environment, than TTC as with TTC relative speed and distance data is required. It represents time difference between the passage of the “offended” and “conflicted” road-users over the area where collision may occur. This makes PET a useful objective and less resource-demanding in contrast of TTC‟s data extraction process. It is so as PET does not involve recalculations at each time-step during a conflict zone or safety warning zone (Archer 2005).
On the other hand the PET-concept is only useful in the case of transversal (i.e. crossing) trajectories in safety critical events. TTC- concept suits for events with similar trajectories. PET-measurement are done on a fixed projected point of collision, rather than one that changes with the dynamics of the safety critical events.
Small PET values indicate that two vehicles have a short distance to one another, whilst zero PET values indicate a collision between two vehicles. Thus PET is a measure of how nearly a collision has been avoided. These considerations led us to adopt PET as our measure of when an incident has or may have occurred, and thus we can draw conclusions about driving behavior in intersections.

Gap Time

If the road user continues with the same velocity and trajectory then the time lapse between the completion of an approaching by a turning road-user and the arrival time of a crossing road-user is known as Gap Time (GT).
The „Gap Time‟ concept estimates the time of arrival at the potential point of conflict in spite of the actual time difference. It relies on a measure at the point when evasive action is first taken. While this accounts for the effect of braking by a secondary vehicle the elementary nature of the original PET concept is lost as resource demanding measures of both speed and distance are required during data extraction process (Archer 2005).

Table of contents :

1. Introduction
1.1. Objective
2. Literature Study
2.1. Simulator Research
2.1.1. Types of Simulated Environments
2.1.1.1. Moving Based Simulated Environment
2.1.1.2. Fixed-Based Simulated Environment
2.2. Factors influencing safety
2.2.1. Effect of Stress on driving behavior
2.2.2. Variations and traffic velocity
2.2.3. Violation of expectations
2.3. Conflict Indicating Variables
2.3.1. Time to Collision (TTC)
2.3.2. Extended Time to Collision (TET, TIT)
2.3.3. Time to Accident (TA)
2.3.4. Post-Encroachment Time (PET)
2.3.5. Gap Time
2.4. Self-report measures
2.4.1. Schwartz’s Configural Model
2.4.2. NEO Five Factor Inventory (Neo-FFI)
2.4.3. Questionnaires on conflict avoidance, time horizon and tolerance of uncertainty
2.4.4. Questionnaire on Locus of control
2.4.5. Driver Behavior Questionnaire (DBQ)
2.4.6. Driver Style Questionnaire (DSQ)
2.5. Data Analyzer Study
3. Research Questions
4. Method
4.1. Participants
4.2. Simulated scenario design study
4.2.1. Apparatus
4.2.2. The Simulator
4.2.3. The Simulated Environment
4.3. Tasks
4.4. Questionnaires
4.5. Procedure
4.5.1. The Cycles
4.5.2. Pilot Study
4.6. Data Collection
5. Analysis and Discussion
5.1. Correlation between Schwartz’s Value Surveys and Questionnaires
5.1.1. Correlations between Schwartz’s value and Conflict Avoidance
5.1.2. Correlations between Schwartz’s value and Impatient Value
5.1.3. Correlations between Schwartz’s value and Hostile Value
5.1.4. Correlations between Schwartz’s value and Open to suggestions
5.1.5. Correlations between Schwartz’s value and Cautious Value
5.1.6. Correlations between Schwartz’s value and tolerance of Uncertainty
5.1.7. Correlations between Schwartz’s value and Frequency of behavior
5.1.8. Correlations between Schwartz’s value and Obedient
5.2. Post Encroachment Time (PET) Analysis
5.2.1. Results
6. Conclusion
6.1. Future Work
7. References
8. Appendix

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