W.J. Schakel

A cooperative system based variable speed limit control algorithm against jam waves - an extension of the SPECIALIST algorithm

The SPECIALIST algorithm can resolve jam waves on freeways using roadside technology: detector loops and speed limit gantries. In this paper we extend the algorithm, enabling the integration with cooperative system technologies and other road side detectors, such as in-car detection and actuation, and video-based monitoring (VBM). Integrating these cooperative elements can provide faster and more accurate jam detection, which leads to a better performance of the SPECIALIST algorithm. For the fusion of the various data sources (loops, VBM, floating car data) an extension of the Adaptive Smoothing Method is used. The data fusion method is also adapted to comply with the input requirements of the SPECIALIST algorithm. The resulting system is suitable for a mixed roadside/in-car detection and actuation environment. The performance of the resulting system is evaluated using the microscopic simulator VISSIM. The results show that floating car data and VBM can considerably improve the jam detection times and the accuracy of the detected jam location, which lead to more efficient speed limit schemes.

Pleasure in using adaptive cruise control: A questionnaire study in The Netherlands

ABSTRACT Objective: Adaptive cruise control (ACC), a technology that allows for automated car following, is becoming increasingly prevalent. Previous surveys have shown that drivers generally regard ACC as pleasant but that they have to intervene when the ACC reaches its operational limits. The former research has been mostly concerned with specific car brands and does not fully reflect the diversity of ACC types in traffic today. The objective of the present research was to establish the determinants of pleasure in using ACC. Methods: A 55-item online questionnaire was completed by Dutch users of diverse ACC systems. Results: Respondents (N = 182) rated their ACC highly, with a mean score of 8.0 on a scale from 1 (extraordinarily negative) to 10 (extraordinarily positive) and were most pleased with ACC on high-speed roads and in low-density traffic. Moreover, the findings point to specific operational limits such as associated with cut-in situations. Pleasure was greater for the types of ACC that are able to decelerate to a full stop, according to 48% of our sample. An analysis of the free-response items indicated that respondents who were displeased with ACC mentioned its occasional clumsiness and the dangerous situations it may evoke, whereas those who were pleased with ACC valued the complementarity of human and machine and emphasized the roles of responsibility and experience in using ACC. Conclusion: Pleasure in using ACC is a function of both technological advances and human factors.

Will automated vehicles negatively impact traffic flow

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in which many yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature. We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

A model of car-following behavior at sags

Sags are bottlenecks in freeway networks. The main reason is that the increase in slope has a negative effect on vehicle acceleration, which results in local changes in car-following behavior that reduce traffic flow capacity. Existing car-following models are not able to reproduce the acceleration behavior of drivers at sags and the resulting traffic flow dynamics in a sufficiently realistic way. This paper presents a new car-following model that aims to fill that gap. The model assumes that drivers have a limited ability to compensate for the negative effect that an increase in gradient has on vehicle acceleration. Compensation is assumed to be linear over time; the maximum compensation rate is defined as a parameter. The paper presents the results of a case study using the proposed car-following model. The study site is a particular sag in Japan. Similar traffic flow patterns are observed in simulation and in empirical data from that site. In particular, the model generates a bottleneck caused by the increase in freeway slope, reproducing its location very accurately.