Automation may be assumed to have a beneficial impact on traffic flow efficiency. However, the relationship between automation and traffic flow efficiency is complex because behavior of road users influences this efficiency as well. This paper reviews what is known about the influence of automation on traffic flow efficiency and behavior of road users, formulates a theoretical framework, and identifies future research needs. It is concluded that automation can be assumed to have an influence on traffic flow efficiency and on the behavior of road users. The research has shortcomings, and in this context directions are formulated for future scientific research on automation in relation to traffic flow efficiency and human behavior.
Parameter identification of microscopic driving models is a difficult task. This is caused by the fact that parameters-such as reaction time, sensitivity to stimuli, etc.-are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new approach to identifying parameters of car-following models.One of the main contributions of this article is that the proposed approach allows for joint estimation of parameters using different data sources, including prior information on parameter values (or the valid range of values). This is achieved by generalizing the maximum-likelihood estimation approach proposed by the authors in previous work.The approach allows for statistical analysis of the parameter estimates, including the standard error of the parameter estimates and the correlation of the estimates. Using the likelihood-ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A nice property of this test is that it takes into account the number of parameters of a model as well as the performance. To illustrate the workings, the approach is applied to two car-following models using vehicle trajectories of a Dutch freeway collected from a helicopter, in combination with data collected with a driving simulator.
Automated vehicles are expected to have a substantial impact on traffic flow efficiency, safety levels, and levels of emissions. However, field operational tests suggest that drivers may prefer to disengage adaptive cruise control (ACC) and resume manual control in dense traffic conditions and for maneuvers such as changing lanes. These so-called authority transitions can have substantial effects on traffic flow. To gain insight into these effects, a better understanding is needed of the relationships between these transitions, longitudinal dynamics of vehicles, and behavioral adaptations of drivers. In this context, a driving simulator experiment was set up to gain insight into the effects of authority transitions between ACC and manual driving on longitudinal dynamics of vehicles. Participants were assigned randomly to one of three conditions. In the control condition, participants drove manually. In the first experimental condition, a sensor failure was simulated at a specific location where drivers were expected to resume manual control. In the second experimental condition, drivers switched ACC off and on by pressing a button whenever they desired. Statistical tests indicated that the distributions of speed, acceleration, and time headway differed significantly between the three conditions. In the first experimental condition, the speed dropped after the sensor failure, and the time headway increased after the discretionary reactivation of ACC. These results seem to be consistent with previous findings and suggest that authority transitions between ACC and manual driving may significantly influence the longitudinal dynamics of vehicles and potentially mitigate the expected benefits of ACC on traffic flow efficiency.
The values on parameters describing longitudinal driving behavior in car-following models differ substantially between drivers. Different individual interactions with the environment are assumed to play an important role, which might be explained through mental workload. Therefore a driving simulator experiment with a repeated measures design was performed to investigate to what extent perception of an incident in the other driving lane influences physiological indicators as well as subjective estimates of mental workload and longitudinal driving behavior. As almost none of the current models of car-following behavior incorporate mental workload as a determinant of driving behavior, an investigation was conducted by using a calibration approach for joint estimation to determine whether these models, represented by the intelligent driver model and the Helly model, adequately described longitudinal driving behavior in case of incidents in the other driving lane. The results indicated that perception of an incident in the other driving lane influenced mental workload as measured by physiological indicators and longitudinal driving behavior. In addition, the results indicated that current car-following models did not adequately describe driving behavior in case of incidents in the other driving lane.
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