Search

Application of dynamic maximum speed limits may lead to positive effects for the environment, safety, and traffic flows. However, the efficacy of this dynamic traffic management measure depends largely on the behavior of drivers (i.e., compliance). In this paper, it is conjectured that compliance does not depend solely on attitudes of drivers but also depends on drivers' perceptions of the dynamic maximum speed limit signs and mental workload. It is assumed that characteristics of the dynamic maximum speed limit signs influence the perceptions of drivers as well as their mental workload. It is, however, not yet clear to what extent characteristics of the signs influence perception, mental workload, and compliance of drivers. Therefore, two driving simulator experiments were performed to investigate the influence of four factors on drivers' perception, mental workload, and compliance. The factors studied were the signs' content, implementation, location, and frequency. From the results, it followed that different effects of these factors could be observed. For example, it was observed that the frequency with which dynamic maximum speed limit signs were provided to the drivers had a significant influence on perception and compliance, although a significant effect on mental workload could not be established. The paper concludes with a discussion of results and recommendations for future research.

Application of dynamic maximum speed limits may lead to positive effects for the environment, safety, and traffic flows. However, the efficacy of this dynamic traffic management measure depends largely on the behavior of drivers (i.e., compliance). In this paper, it is conjectured that compliance does not depend solely on attitudes of drivers but also depends on drivers’ perceptions of the dynamic maximum speed limit signs and mental workload. It is assumed that characteristics of the dynamic maximum speed limit signs influence the perceptions of drivers as well as their mental workload. It is, however, not yet clear to what extent characteristics of the signs influence perception, mental workload, and compliance of drivers. Therefore, two driving simulator experiments were performed to investigate the influence of four factors on drivers’ perception, mental workload, and compliance. The factors studied were the signs’ content, implementation, location, and frequency. From the results, it followed that different effects of these factors could be observed. For example, it was observed that the frequency with which dynamic maximum speed limit signs were provided to the drivers had a significant influence on perception and compliance, although a significant effect on mental workload could not be established. The paper concludes with a discussion of results and recommendations for future research.

The aim of this study was to investigate the effects of providing travel information to drivers about a traffic jam ahead and a potential detour or short-cut. Two groups of participants, native and non-native Dutch speakers were requested to drive in a driving simulator under both calm and dense traffic conditions. Travel-information was presented by means of three nomadic systems; in visual mode on a PDA and on a mobile phone via SMS, and through a mobile phone in auditory mode via the (simulator mock-up) vehicle’s audio system. The results showed that with regard to usability the SMS message was evaluated worse than the other two systems, while with respect to cognitive processing, SMS caused more subjective (i.e. experienced) workload than the other two systems. Native participants believed any information-providing system to be less useful than non-native participants did. All participants remembered more of the information when traffic was dense whereas natives remembered more than non-natives. With regard to performance and safety, driving performance was better when traffic was calm, as compared to dense traffic; however, compensation was shown by lowering driving speed in the latter condition. After participants were provided with travel information, their driving performance with respect to the consequences of distraction differed between systems. The auditory information provision system allowed the best driving performance; the other two systems required the participants to look away from the road (too) long compromising safety, while reading an SMS took longer than scanning a PDA.

Summary: The residual effects of lormetazepam 1 mg and 2 mg in soft gelatine capsules on driving performance were assessed and compared to those of flurazepam 30 mg, which is also a powerful hypnotic, but possesses a far less favourable pharmacokinetic profile with a longacting sedative metabolite. Driving performance was tested 10 to 11 h and 16 to 17 h post administration, after 2 days on placebo (baseline), and 2,4 and 7 days of drug treatment (active), and after 1 and 3 days following the resumption of placebo (washout). The driving test consisted of operating an instrumented motor-vehicle over a 72 km highway circuit in light traffic. Flurazepam 30 mg significantly impaired the ability to control the lateral position of the vehicle compared to placebo baseline measurements. The degree of impairment was substantial in the female subjects and was greater in the morning than in the afternoon. Lormetazepam 1 mg showed no residual effect on driving performance. Lormetazepam 2 mg impaired driving performance to some extent on the following morning, 10 to 11 h post administration, but no residual effect was found in the afternoon. All drugs improved sleep quality and prolonged sleep duration to more or less the same extent.

Motor processes partly determine reaction time (RT) in both choice reaction time and in binary classification tasks. These latter tasks are popular in cognitive psychology because the experimenter believes that he has kept the motor component simple and constant and therefore can attribute changes in RT to perceptual or cognitive processes. In this paper we used the P3 component of the event-related potential (ERP) as a time marker indicating the duration of perceptual and cognitive processes. The latency of this component is believed to reflect stimulus evaluation time independent of response selection and organization time.