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Chapter 10: thesis traffic psychology


This is chapter 10 of the thesis from 1996 by van Winsum. It concerns a number of behavioural studies into driver adaptation that have been performed in a research driving simulator.

Other chapters of this thesis can be found here:


General discussion and conclusions


10.1 Testing the adaptation model for the case of individual differences: discussion of results from the experiments


The adaptation model predicts that factors  that affect operational performance will normally result in an adaptation of behaviour on the tactical level, such that constant safety margins are maintained. Individual differences in operational performance are then predicted to be reflected in individual differences in behaviour on the tactical level. The results of the experiments support the adaptation model applied to the relation between individual differences in behaviour on the tactical level and the operational level for both the car-following task and the curve negotiation task. The general results from these experiments and the relevance for the adaptation model are discussed in this chapter.

If individual differences in skills and operational performance result in adaptation of behaviour on the tactical level then this behaviour must be consistent and characterized by individual differences. This implies that, in addition to the transient effects on tactical behaviour discussed in previous paragraphs, some level of consistency and constancy must exist in, for example, speed choice and choice of headway during car-following. If the adaptation model also applies to individual differences then at least some part of the between-subjects variance in behaviour on the tactical level must be explained in terms of the between-subjects variance in operational performance. The tasks of curve negotiation and car-following were selected for closer examination. Speed choice during curve negotiation is considered as an example of the effect of lateral control performance on behaviour on the tactical level. Choice of time-headway in car-following is described as an example of the effect of longitudinal control performance on tactical behaviour.

Experiment 1 deviates from the other five in that it not only examines the effect of individual differences in operational performance but also the effect of a situational factor, i.e. curve radius. The results have been published in Human Factors. The experiment was performed in a driving simulator that was programmed by the people from Carnetsoft. Explaining speed choice as a function of curve radius has been a long lasting problem that has been investigated in a large number of studies. The problem is usually described in terms of a relation between lateral acceleration and choice of speed. The underlying process has never become clear. However, the results of experiment 1 clearly suggest that the inverse relation between lateral acceleration and speed, often referred to in the literature, is the result of a process of adaptation of speed choice together with a strategy of maintaining constant safety margins. Speed choice in curves proves to be a consistent measure of tactical behaviour. Also measures of operational performance prove to be stable and consistent within the driver. This indicates that the important prerequisite for the validity of the adaptation model that both operational performance and behaviour on the tactical level are consistent and characterized by clear individual differences is fulfilled for the case of lateral control performance and speed choice in curves. Steering is discussed as the factor that affects choice of speed in curves. A model of steering is presented that suggests that steering errors are affected by individual differences in steering competence and by required steering-wheel angle. A larger required steering-wheel angle then results in larger steering errors. The situational factor road radius, together with speed, affects required steering-wheel angle. A smaller radius increases required steering-wheel angle and thus steering error, which is compensated or adapted for by choosing a lower speed. The same reasoning applies to individual differences in steering performance. This is measured independently during straight road driving. Drivers with poorer steering competence are characterized by larger steering errors which is compensated for by choosing a lower speed in curves according to the adaptation model. Summarizing, in experiment 1 the adaptation model is tested in two different manners for the case of speed choice in curves:

– curve radius affects operational performance which affects speed choice, and

– steering competence affects operational performance which affects speed choice.

These hypotheses are supported by the results of experiment 1. The results indicate that a smaller curve radius and poorer steering competence increase steering errors and result in such speed reductions that TLC is kept on a constant minimum value. These results then strongly support the adaptation model discussed in paragraph 2.5 and the value of the concept of a time-based safety margin that is controlled during driving.


Experiments 2 to 6 consider the task of car-following. During car-following the driver never knows whether the lead vehicle will brake, and if it does, how hard it will brake and for how long. It is then assumed that the driver has learned the quality of his or her braking performance from previous experiences and that this results in the choice of a preferred  time-headway (THW). THW is the time available to the driver to reach the same level of deceleration as the lead vehicle in case it brakes, without becoming involved in a collision. Braking performance is assumed to affect the time required to reach the same level of deceleration as the lead vehicle. Adaptation of THW may then be regarded as a compensation strategy for drivers with poorer braking performance.

The detailed examination of the car-following task introduces some specific problems. First of all, it is not immediately clear which aspects of operational performance play a role in choice of time-headway. This is examined in the experiments 2 to 6.

Secondly, the literature on choice of THW during car-following is not very extensive. The literature on braking is limited as well and mainly restricted to emergency braking (braking as fast as possible), see chapter 5. This implies that the theoretical perspective on braking and car-following had to be developed during the course of experimentation and that the number of experiments required to test the theoretical model is larger for the case of car-following than for speed choice in curves.

Thirdly, an important limitation in the study of car-following is that the details of operational braking performance can only be compared between different drivers if they start braking at the same distance- or time-headway. This means that, in studying braking performance, drivers will have to be forced into time-headway conditions they would not choose themselves, which may result in differential effort allocations as a function of the discrepancy between preferred THW and actual THW. This was illustrated by the results of an experiment by Heino et al. (1992). They found that particularly drivers who normally follow at a larger THW increase their mental effort, as measured by heart rate variability, when they are forced to follow at a smaller THW. This means that the methodological prerequisite of measuring braking performance in forced-paced situations may, to some degree, obscure individual differences in braking performance because of between-subjects differences in effort allocation. Nevertheless in the present studies, this method is preferred to the alternative where braking performance is measured while drivers follow at their preferred THW. Drivers who follow at a smaller THW would be forced to brake faster compared to drivers who follow at a larger THW, and this would damage the comparability of braking performance between drivers.

The results of experiments 3 and 4 demonstrate that choice of THW is consistent and constant over different speeds. In experiment 3 preferred THW is measured at speeds of 40, 50, 60 and 70 km/h. Speed has no significant effect on preferred THW and the within-subjects reliability of the THW’s is high. This is confirmed by the results of experiment 4. The high consistency in choice of THW has been confirmed in an on-road experiment by Heino et al. (1992). They reported a correlation of 0.85 between time-headways measured on two different stretches of road. Other studies on the consistency of THW are discussed in chapter 6. The results indicate that choice of THW is independent of speed and consistent within the individual driver and that clear and reliable individual differences exist in choice of THW. This is an important prerequisite for the application of the adaptation model to individual differences.


Experiment 2 examines the relation between preferred THW and the ability to brake as fast as possible,  the speed of stimulus encoding and response preparation. The additive factor logic (see Sternberg, 1969) is applied to examine the locus of effect of operational performance differences. The search for differences in the ability to brake as fast as possible stems from the tradition in the literature on braking where the quality of braking is generally examined in terms of the ability to brake as fast as possible. Experiment 2 may therefore be regarded as a search for individual differences in the limits of performance. The braking parameter that is studied in detail is reaction time (RT), defined as the interval between the moment the lead vehicle starts to brake and the moment the subject starts to release the foot from the accelerator. Again, this approach stems from the dominant view in the literature on braking, i.e. that differences in braking performance originate from perceptual factors measured by RT. Differences in the speed of stimulus encoding regarding the braking action of the lead vehicle would suggest that drivers with a smaller preferred THW (short followers) perceive the braking of the lead vehicle earlier. Differences in response preparation would suggest that the state of motor readiness is reached earlier by short followers compared to long followers. The results indicate that choice of THW is not related to individual differences in RT for a decelerating lead vehicle, to differences in stimulus encoding or to differences in response preparation. From this it is concluded that differences between short and long followers cannot be explained in terms of “limits of perceptual and motor skills”. However, differences in preferred THW appear to be related to braking performance in quite another way. Differences in response execution speed as a function of preferred THW are restricted to braking situations characterized by uncertainties concerning the braking of the lead vehicle, the required deceleration and the duration of braking, as is always the case in real world car-following situations. The results suggest that individual differences in the transformation of visual feedback to the motor response may be related to choice of THW. The results have been published in Ergonomics.


Experiment 3 considers these aspects in more detail and examines the use of time-to-collision (TTC) during braking and the way the braking response is executed. The process of braking is connected explicitly to the literature on time-to-collision (TTC). TTC is defined as the time required for two vehicles to collide if they continue at their present speed and on the same path (see for example Van der Horst, 1990). In the literature it is often suggested that the perception of TTC from the optic flow field is an important skill related to the initiation of braking. But curiously, only a few experimental studies have connected the concept of TTC to the braking response. The general conclusion from the literature is that TTC is underestimated and that there are large individual differences in the ability to accurately estimate TTC. In experiment 3 the hypothesis is tested that preferred THW is related to the sensitivity to TTC information. According to this reasoning, drivers who are more sensitive to TTC information are better able to judge the moment to start braking while drivers who are less sensitive to TTC information run the risk of starting to brake too late. This may result in a compensatory larger preferred time-headway for these drivers. The results indicate that both the initiation and the control of braking are strongly determined by TTC on the moment the lead vehicle starts to brake. Short followers differ from long followers in the control of braking: short followers brake harder and more efficiently, and, most importantly, the intensity of braking is more sensitive to TTC differences, compared to long followers. Yet, a confounding factor may have affected the results. Because the absolute levels of TTC differ between short and long followers in this experiment, short followers may have been forced to brake more efficiently.


Experiment 4 explicitly controls this confounding factor. Braking performance is measured with identical initial time-headway for all subjects. The subjects are unaware of the fact that the lead vehicle will brake and of the required deceleration and the duration of braking. A model of braking is discussed in which the process of braking is divided into three separate phases: the RT phase, the open-loop ballistic phase and the closed-loop phase. The RT phase is defined as the interval between the moment the lead vehicle starts to brake and the moment the foot starts to be retracted from the accelerator pedal. The open-loop phase is operationally defined as the period that starts when the subject retracts the foot from the accelerator after the lead vehicle has started to decelerate and ends when the brake pedal is touched. During the closed-loop phase visual feedback is used to control the process of braking. It is defined operationally as the period between the moment the brake pedal is touched by the foot and the moment the maximum brake position is reached. It is hypothesized that the speed of the open-loop ballistic response is determined by TTC on the moment the driver detects the deceleration of the lead vehicle, while the duration of the closed-loop phase is determined by the number of decelerations in the brake pedal signal (movement corrections). The results show that reaction time is not related to preferred time-headway. This confirms the results of the experiment 2. The open-loop phase of the motor response appears to be very sensitive to TTC, and especially to TTC on the moment the foot is retracted from the accelerator pedal. This supports the hypothesis. Also, the results indicate that short followers are characterized by a faster open-loop response that is not caused by a smaller TTC. This suggests that short followers program their movement speed to a higher level compared to long followers. The duration of the closed-loop phase of the motor response is, in accordance with the hypothesis, strongly related to the number of movement corrections. Short followers exhibit a faster closed-loop response with fewer movement corrections. The results also indicate a strong effect of total movement time on preferred THW, strengthening the conclusion that short and long followers differ in both the open- and closed-loop phases of movement. This suggests that short follo­wers are more sensitive to the task requirements in braking situations, confirming the results of experiment 3.


Experiments 5 and 6 test the hypothesis that short followers differ from long followers in the sensitivity of the braking response execution to TTC information. Both experiments apply the model of braking discussed in chapter 7. In experiment 5, the RT phase, the open-loop and the closed-loop phases of the braking process are manipulated independently. If short followers differ from long followers in either of these phases then the factor “preferred THW” should interact with any factor that manipulates these phases. The RT phase is manipulated with the factor initial THW on the moment the lead vehicle starts to brake. The duration of the open-loop phase is manipulated by the factor initial deceleration. The level of deceleration (3 vs. 6 m/s²) of the lead vehicle is expected to affect the TTC on the moment the subject detects the braking of the lead vehicle and thereby the duration of the open-loop phase. The closed-loop phase is manipulated by the factor secondary deceleration: as soon as the foot touches the brake pedal (this is the moment the closed-loop phase starts) the deceleration of the lead vehicle changes. This requires the use of visual feedback in order to change the programmed motor response. Although the results show that the respective phases of the braking response are affected by the manipulations, the predicted interactions of preferred THW with the factors that manipulate the open- and the closed-loop phases are not statistically significant. The pattern of results suggests that task-specific factors resulted in undesirable startle reactions and vigilance effects.

Because of this the final experiment 6 applies multiple measurements per manipulated factor, a higher frame-rate and shorter task duration, in order to prevent startle reactions and vigilance effects. The main hypothesis is that short followers differ from long followers in the sensitivity of the motor response to TTC. TTC is manipulated with two levels of initial deceleration of the lead vehicle (3 vs. 6 m/s²), in random order. The results indicate, in support of the main hypothesis, that the open-loop response of short followers is more sensitive to differences in TTC compared to long followers. The assumed causal chain is that individual differences in some basic perceptual-motor skill affect the quality of the braking response. The driver is assumed to adapt the choice of THW during car-following accordingly. In this way drivers protect themselves against poorer operational performance. However, it may be argued that short followers have had more practice in braking resulting in improved operational performance because of learning effects. To rule out this explanation it is examined whether short followers differ from long followers in perceptual-motor performance in tasks unrelated to braking. In order to test whether choice of THW is related to more general perceptual-motor skills that require the transformation of visual information to a motor response, performance on a lateral tracking task and a longitudinal tracking task is tested. The results clearly indicate that short followers perform better on both the lateral tracking tasks and the longitudinal tracking task. In addition to this, performance on both types of tracking tasks is significantly correlated. This strongly suggests that:

1) Short followers differ from long followers in perceptual-motor skills related to the transformation of visual information to a motor response,

2) these differences in skill are not acquired as a function of differences in following behaviour,

3) these differences in skill affect the quality of braking performance in the sense that short followers tune the braking response better to the requirements of the situation, giving them a higher sense of control,

4) resulting in the choice of a larger time-headway for drivers with poorer operational performance and a smaller time-headway for drivers with better operational performance.


10.2 General conclusions and next steps


The impact of vehicle factors and situational factors related to road, weather and temporary state on driver behaviour and the underlying mechanisms of behavioural effects have been addressed in this study. Mechanisms related to individual differences in driver behaviour have been tested from the perspective of the adaptation model. It is clear that the system components vehicle and environment have an important effect on driver behaviour, mediating the effects on accident involvement and traffic safety in general. Adaptation mechanisms are best studied by measuring driver behaviour as a function of vehicle factors, individual differences in skills, situational factors and temporary states instead of accidents, because these factors affect behaviour directly while they affect accident involvement indirectly. One of the reasons for the lack of progress in driver modeling, referred to in chapter 1, is the abundance of determinants and factors that operate simultaneously. This has resulted in several theories that apply only to a limited problem domain. The adaptation model integrates the operational and the tactical level of driver behaviour into one framework. As discussed in chapter 2, driver models and studies in traffic psychology usually examine only one of these levels. It is suggested that these levels should always be studied in their mutual relationship. For example, if the effect of a roadmeasure on speed is examined it should also examine the effects on operational performance at the same time. Of course practical problems may prevent this and this is one of the reasons why simulators may be useful. However, the results suggest that measurement of behaviour on one level may be meaningless when behaviour on another level is excluded from examination. Several other driving tasks such as speed choice on straight roads, gap acceptance at intersections, stopping for traffic lights, overtaking and so on need to be examined within this framework.

According to the adaptation model, drivers with poorer operational performance protect themselves by adapting behaviour on the tactical level, resulting in a lower speed or larger time-headway. The other side of this reasoning is that drivers with better perceptual-motor skills and good operational performance drive at higher speeds or follow at smaller time-headways. However, it is not by any means intended to suggest that drivers with higher speeds are not dangerous because they have a highly developed skill level. Undoubtedly, some drivers who follow other vehicles at a close distance or who drive faster than average are not characterized by better operational performance. The suggested relation is a probabilistic one, and not mechanistic. However, the line of reasoning makes clear that the concept of risk becomes more meaningful if skills and level of performance are added to the equation. This is to say that a certain speed may not be as risky for one person as for the other if they differ in certain required perceptual-motor skills, from the same perspective as the fact that flying an F16 fighter plane is considerably more risky for the author of this thesis than for an experienced pilot.

Also, it is often assumed that higher speeds and shorter following distances are associated with a high accident risk although a number of studies do not confirm this simple relation. The effect of variabi­lity within the traffic system on accidents is one of the reasons why Summala (1985) promoted the introduction of speed limits. This has greatly reduced the accident risk in a number of countries. Speed limits reduce the variability of speed in the system and this reduces accident risk. Brehmer (1990) predicted that accident probability is lowest for cars driving with the average speed, but increa­ses for drivers who deviate more from the average speed, either to lower or higher speeds. He referred to a study of Solomon (1964) on the relation between speed and accident rate on US highways, that supported this hypothesis. Munden (1967), referred to in Rooyers et al. (1992), repor­ted a U-shaped relation between speed and accident rate as well. Brehmer also predicted that accident rates are higher in environments where the variance of the speed distribu­tion is highest. A study of Greenberg (1964) was referred to which demon­strated a positive correlation between accident rate and speed distribution for a sample of US roads. Numerous authors have mentioned that it is an establis­hed fact that accident risk is related to driver speed, and that speeding therefore can be regarded as a form of driver error, related to poor speed perception skills or poor hazard perception. However, whether a higher speed is riskier compared to a lower speed with identical speed distributions is an unresol­ved matter. A similar point can be raised with regards to headways during car-following. Shorter time-headways are usually associated with a higher risk of rear-end collisions. In a large-scale study on the relation between time-headway and accident risk in several countries a relation was found between rear-end accident rates per 100 million vehicle kilome­ters and time-headway (Benjamin, 1980). This relation was however strongly affected by the flow of traffic or traffic density. Traffic volume affected both time-headway and the number of rear-end collisions so that a causal relationship between close following behaviour and rear-end acci­dents could not be established. It was already demonstrated in the fifties by the studies of Herman (referred to in Forbes, 1972) that, even in car-following situations with conservative headways, normal speeds and short response times of drivers, flow distur­bances by the platoon leader (the first car in the chain) may cause a chain reaction that makes it impossible for drivers downstream to avoid a collision. This indicates that the relation between speed choice, choice of time-headway and accident risk is not as straightforward as often suggested.

The general principle of behavioural adaptation demonstrates the inherent flexibility of human behaviour. This flexibility resembles the issue of ‘human behaviour feedback’, discussed in chapter 1, which has puzzled many traffic safety researchers and triggered fierce discussions about the effects of safety measures. The adaptation model may offer the concepts and methodology to clarify the issue of this ‘human behaviour feedback’ in more coherent terms. Driver adaptation of tactical behaviour to effects of safety measures on operational performance may be an important determinant for the success or failure of intended safety changes in the road-vehicle-driver system.

Although the process of adaptation appears to be ‘normal behaviour’, it also seems clear that certain factors prevent adaptation resulting in increased accident involvement. Examples of this are the consumption of alcohol and the case of the young male driver. Citing from paragraph 2.3.4: “The interaction of BAC level and age on accident involvement suggests that both factors share a common locus of effect, in the sense that the factor that causes the higher accident rate of young drivers is aggravated by alcohol. In the discussion of the effects of alcohol it was suggested that the lack of compensation for impaired performance may be the cause for the large role of alcohol in accident causation. Evidence was presented that drivers are unaware of performance decrements under alcohol which is possibly the cause for the absence of compensatory speed changes and effort. From the same perspective it may be suggested that young and inexperienced drivers have not yet learned to recognize the effects of situational factors on their perfor­mance and thus fail to compensate for these effects resulting in speeds that are too high for the circumstances”. Clearly this is an issue that needs to be investigated further. There are some indications that alcohol inhibits the perception of feedback from the driving task. The assumed lack of adaptation in young (male) drivers also needs to be explored further. The theory presented in this study offers a framework to examine these issues.

An important next step is the further validation and testing of the adaptation model. In the present study only a limited part of the model was tested. For example, the principle of effort allocation under forced paced conditions and the effects of this on operational performance need to be tested in further studies. The six experiments described in this study are only a first step in the direction of testing the limits and scope of the model of adaptation.


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