A time-discrete harmonic oscillator model of human car-following

Wagner, Péter

doi:10.1140/epjb/e2011-20722-8

Cited by 32 publications

(23 citation statements)

References 7 publications

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“…By now a large amount of empirical and experimental data were collected about car following in the reality (see, e.g., [14]). In particular, papers [15,16,17,18,19] have analyzed the data recorded by RTK-DGPS (realtime kinetics, differential global positioning system) on a Japanese test track [20] and the single car data from the German freeway A1 [21]. In particular, the headway distribution P (h), the relative velocity distribution P (u), and the acceleration distribution P (a) are used in this analysis.…”

Section: Introductionmentioning

confidence: 99%

Statistical Properties of Car Following: Theory and Driving Simulator Experiments

Ando

Lubashevsky

Zgonnikov

et al. 2015

Stochastic Systems Theory and its Applications (SSS)

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A fair simple car driving simulator was created based on the open source engine TORCS and used in carfollowing experiments aimed at studying the basic features of human behavior in car driving. Four subjects with different skill in driving real cars participated in these experiments. The subjects were instructed to drive a car without overtaking and losing sight of a lead car driven by computer at a fixed speed. Based on the collected data the distributions of the headway distance, the car velocity, acceleration, and jerk are constructed and compared with the available experimental data for the real traffic flow. A new model for the carfollowing is proposed to capture the found properties. As the main result, we draw a conclusion that human actions in car driving should be categorized as generalized intermittent control with noise-driven activation. Besides, we hypothesize that the car jerk together with the car acceleration are additional phase variables required for describing the dynamics of car motion governed by human drivers.

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Section: Introductionmentioning

confidence: 99%

Statistical Properties of Car Following: Theory and Driving Simulator Experiments

Ando

Lubashevsky

Zgonnikov

et al. 2015

Stochastic Systems Theory and its Applications (SSS)

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“…They operate, in particular, with such notions as the optimal velocity for a given headway and the velocity difference. The linearizion of the corresponding governing equations gives rise to the harmonic oscillator model which can capture a number of fundamental properties of car dynamics [10,27,28]. Some of these models explicitly allow for the delay in human reaction and take the form of delay-differential equations.…”

Section: Discussionmentioning

confidence: 99%

Extended phase space description of human-controlled systems dynamics

Zgonnikov

Lubashevsky

2014

Progress of Theoretical and Experimental Physics

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Humans are often incapable of precisely identifying and implementing the desired control strategy in controlling unstable dynamical systems. That is, the operator of a dynamical system treats the current control effort as acceptable even if it deviates slightly from the desired value, and starts correcting the actions only when the deviation has become evident. We argue that the standard Newtonian approach does not allow to model such behavior. Instead, the physical phase space of a controlled system should be extended with an independent phase variable characterizing the operator motivated actions. The proposed approach is illustrated via a simple non-Newtonian model capturing the operators fuzzy perception of their own actions. The properties of the model are investigated analytically and numerically; the results confirm that the extended phase space may aid in capturing the intricate dynamical properties of human-controlled systems.

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“…The procedure for AP identification was taken from Brackstone et al (2002); similar procedures have been proposed by other authors (Wagner, 2011;Hoogendorn et al, 2011). All these techniques base identification of APs on kinematic conditions; identification of these points is straightforward if one analyse trajectories in the phase-plane (as in Figure 3 left side).…”

Section: Identification Of Candidate Action Pointsmentioning

confidence: 99%

“…As well addressed also in Saifuzzaman and Zheng, even if several attempts to embed human behaviours into Engineering models have been carried out (recently Pariota et al, 2015 on-line publication), psycho-physical paradigms are characterised by quite peculiar (and convincing) fundamental assumptions on the human behaviour. As an example, drivers are assumed to adopt a satisficing performance evaluation strategy, rather than an optimal one (Boer, 1999), that means humans are often incapable of identifying and implementing optimal control strategy (Zgonnikov and Lubashevsky, 2014); moreover, are they can be observed to do not apply a continuous control (Wagner, 2011).…”

Section: Introductionmentioning

confidence: 99%

Experimental evidence supporting simpler Action Point paradigms for car-following

Pariota

Bifulco

2015

Transportation Research Part F: Traffic Psychology and Behaviou

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. IntroductionModelling driving behaviour represents a crucial task for many applications in transportation. Three main areas can particularly benefit from an enhanced knowledge of driving behaviour: accident analysis and prevention, microscopic simulation of traffic, and Intelligent Transportation Systems (ITS). Benefits for ITS are mainly expected in the field of Advanced Driver Assistance Systems (ADAS), where some assistance/control logics interact with drivers (and their behaviour) and where both drivers' expectations, and impacts of the innovations on drivers' behaviour have to be considered in order to improve: a) the effectiveness of the solutions; b) driving (and traffic) safety and c) acceptance of technological solutions.Modelling of driving behaviour is based on two fundamental requirements. On the one hand, theoretical frameworks and paradigms are needed. On the other, observation tools and data are required in order both to develop/validate theories and to identify modelling parameters for practical applications. If the research focus is on disaggregate driving behaviour rather than aggregate traffic behaviour, the best source of information is based on individual vehicle data (IVD), as typically obtained by instrumented vehicles (IVs). An IV can be described as a standard vehicle where the kinematics, the interaction with surrounding vehicles and the vehicle-driver interaction are recorded for subsequent analysis. The possibility of observing only the kinematics of IVs, as allowed by some camera-based microscopic roadside observation systems like in the NGSIM project (Alexiadis et al., 2004), can lead to a reduced understanding of driving behaviour. Indeed, the possibility of observing the kinematics of an IV is just a prerequisite and IVs are usually equipped with a large number of sensors. Multisensing approaches not only enhance the estimation of the ego-kinematics of the controlled vehicle (Bifulco et al., 2011), but also allow detection of the surrounding traffic conditions and direct monitoring of on-board interaction between the driver and the vehicle, generally via the controlled area network (CAN). The overall result is a more comprehensive observation and enhanced understand...

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A time-discrete harmonic oscillator model of human car-following

Cited by 32 publications

References 7 publications

Statistical Properties of Car Following: Theory and Driving Simulator Experiments

Statistical Properties of Car Following: Theory and Driving Simulator Experiments

Extended phase space description of human-controlled systems dynamics

Experimental evidence supporting simpler Action Point paradigms for car-following

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