Empirics of Multianticipative Car-Following Behavior

Hoogendoorn, Serge P.; Ossen, Saskia; Schreuder, Marco

doi:10.1177/0361198106196500112

Cited by 43 publications

(18 citation statements)

References 9 publications

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“…Most "ideal" models can, of course, straightforwardly be augmented to include reaction times, and there are approaches that include more sophisticated mechanisms. Socalled psycho-spacing (or action point) models [74] incorporate drivers' inertia to observe and respond to small changes in stimuli, whereas multianticipatory models [75,76] include terms for anticipation of drivers to traffic conditions further downstream. Other attempts describe more sophisticated human-factor mechanisms.…”

Section: Discussion: Modelling and Simulating Automated Vehiclesmentioning

confidence: 99%

Will Automated Vehicles Negatively Impact Traffic Flow?

Calvert

Schakel

Lint

2017

Journal of Advanced Transportation

158

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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.

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Section: Discussion: Modelling and Simulating Automated Vehiclesmentioning

confidence: 99%

Will Automated Vehicles Negatively Impact Traffic Flow?

Calvert

Schakel

Lint

2017

Journal of Advanced Transportation

158

View full text Add to dashboard Cite

show abstract

“…multiple anticipative driving strategy). Such multiple (forward) anticipations are important in both human driven cars (Lenz, Wagner, and Sollacher 1999;Treiber, Kesting, and Helbing 2006;Hoogendoorn, Ossen, and Schreuder 2006) or cooperative cruise control (van Arem, Van Driel, and Visser 2006;Arnaout and Bowling 2011;Ngoduy 2013b). This paper concerns the extension of the model of Zheng, Jin, and Huang (2015) to capture such multiple (forward) anticipations.…”

Section: Multi-anticipative Bidirectional Microscopic Modelsmentioning

confidence: 99%

“…Recent empirical study by Hoogendoorn, Ossen, and Schreuder (2006) based on data collected at a small freeway section in the Netherlands has indicated that M = 3 is a reasonable value. However, this is just an indication for a certain freeway section during a certain period of congested traffic.…”

Section: Multi-anticipative Bidirectional Microscopic Modelsmentioning

confidence: 99%

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Ngoduy

Jia

2016

Transportmetrica B: Transport Dynamics

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Recent development of information and communication technologies (ICT) has enabled vehicles to timely communicate with others through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Such cooperative driving has motivated many studies to model the dynamics of traffic flow under multi anticipative driving strategy (i.e. drivers react to many leading vehicles) or bidirectional strategy (i.e. drivers react to both (single) leading and following vehicles). In the vast literature of traffic flow theory, there are continuum models considering multiple forward anticipative strategy, where the driver reacts to many leaders. To the best of our knowledge, few study effort has been undertaken to include bidirectional driving strategy, where the driver reacts to both direct leader and direct follower, in the continuum traffic flow models. Moreover, the current bidirectional continuum traffic flow model still suffers some drawbacks: considers the behaviour of only a single leading vehicle in the forward looking strategy and neglects the impact of the forward space headway (e.g. the distance between two consecutive vehicles) sensitivity parameter on the (linear) stability of the model. This paper aims to derive a continuum traffic model considering both multiple forward and backward driving strategy. It is shown that the derived model is a generalized version of a current continuum model for ITS and can improve important properties of such bidirectional (continuum) model.

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“…Drivers in standard cars have been assumed to consider three vehicles infront in car-following situations, i. e. we set n = 3. Car-following models including three leaders have been found to fit empirical data better than models including fewer vehicles in-front (Hoogendoorn et al, 2006).…”

Section: Car-following Modelling Of Acc and Human Driversmentioning

confidence: 99%

Vehicle Trajectory Effects of Adaptive Cruise Control

Tapani

2011

Journal of Intelligent Transportation Systems

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Adaptive Cruise Control (ACC) is assumed to have a potential to improve quality-of-service and safety and to reduce the environmental impact of the road traffic system. This paper use vehicle trajectories from traffic simulation to study impacts of ACC on vehicle acceleration and deceleration rates. The analysis is based on traffic simulations with car-following models including ACC functionality and driver behaviour in ACC-equipped as well as standard non-equipped vehicles. The simulation results show that ACC can improve the traffic situation in terms of reduced acceleration and deceleration rates even though macroscopic traffic properties may remain uninfluenced. This supports the hypothesised positive road safety and environmental effects of ACC. It is also established that the results are largely dependent on the assumptions made regarding driver behaviour in ACC-equipped and standard vehicles. It is consequently crucial to include appropriate assumptions regarding driver behaviour in traffic simulation based analyses of ACC.

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Empirics of Multianticipative Car-Following Behavior

Cited by 43 publications

References 9 publications

Will Automated Vehicles Negatively Impact Traffic Flow?

Will Automated Vehicles Negatively Impact Traffic Flow?

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Vehicle Trajectory Effects of Adaptive Cruise Control

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