Validation of Microscopic Traffic Models Based on GPS Precise Measurement of Vehicle Dynamics

Apeltauer, Tomáš; Macur, Jiri; Holcner, Petr; Radimsky, Michal

doi:10.7307/ptt.v25i2.1293

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…In addition, they may have very specific meanings and requirements for specific products, regulations, and industries. Some examples: Software [11], Food and drug, Health care [12], Greenhouse gas [13], Traffic and transport [14], Simulation models [15], ICT industry, Civil engineering [16], Economics, Accounting, Agriculture, Arms control.…”

Section: Validation Verification and Evaluationmentioning

confidence: 99%

Validation Strategy as a Part of the European Gas Network Protection

Řehák¹,

Hromada²,

Gkotsis³

et al. 2021

Issues on Risk Analysis for Critical Infrastructure Protection

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The European gas network currently includes approximately 200,000 km high pressure transmission and distribution pipelines. The needs and requirements of this network are focused on risk-based security asset management, impacts and cascading effects of cyber-physical attacks on interdependent and interconnected European Gas grids. The European SecureGas project tackles these issues by implementing, updating, and incrementally improving extended components, which are contextualized, customized, deployed, demonstrated and validated in three business cases, according to scenarios defined by the end-users. Just validation is considered to be a key end activity, the essence of which is the evaluation of the proposed solution to determine whether it satisfies specified requirements. Therefore, the chapter deals with the validation strategy that can be implemented for the verification of these objectives and evaluation of technological based solutions which aim to strengthen the resilience of the European gas network.

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Section: Validation Verification and Evaluationmentioning

confidence: 99%

Validation Strategy as a Part of the European Gas Network Protection

Řehák¹,

Hromada²,

Gkotsis³

et al. 2021

Issues on Risk Analysis for Critical Infrastructure Protection

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“…Connected with the above-mentioned, transport models used in Road Transport networks can be sub-divided into microscopic or macroscopic groups. Microscopic models are oriented on the mutual interaction of drivers, in the context of a complex system as a whole in which a significant role is played by their dynamic characteristics (Apeltauer et al 2013). Macroscopic models are oriented on road networks' global pararmeters and ignore the individual driver characteristics from the perspective of the problematical responses of a road network infrastructure as a whole, on the failure of its subsidiary elements, and are therefore, an optimal tool.…”

Section: Macroscopic Models As a Starting-point For The Dynamic Modelmentioning

confidence: 99%

Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool

Řehák

Radimsky

Hromada

et al. 2019

Administrative Sciences

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Crisis management must provide data to allow for real-time decision-making. Accurate data is especially needed to minimize the risk of critical infrastructure failure. Research into the possible impacts of critical infrastructure failure is a part of developing a functional and secure infrastructure for each nation state. Road transport is one such sector that has a significant impact on its functions. When this fails, there may be a cascading spread of impacts on the energy, health, and other sectors. In this regard, this paper focuses on the dynamic modeling of the impacts of critical road infrastructure failures. It proposes a dynamic modeling system based on a stochastic approach. Its essence is the macroscopic model-based comparative analysis of a road with a critical element and detour roads. The outputs of this system are planning documents that determine the impacts of functional parameter degradation on detour roads-not only applicable in decision-making concerning the selection of the optimal detour road, but also as a support mechanism in minimising possible risks. In this article we aim to expand the extent of knowledge in the Crisis management and critical infrastructure protection in the road transport sector fields.

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“…According to other works [8,9,11,12,13] these parameters and values are the following: b = 2 m/s 2 ; v 0 = 15,28 m/s (55 km/h); δ = 4 and L = 4 m. So, only the remaining three parameters a, T and s 0 will be subject to calibration. It will be shown that our aim can be achieved by the calibration of these three parameters.…”

Section: The Intelligent Driver Modelmentioning

confidence: 99%

“…Recently an improved version of the Wiedemann model was developed [7], which is more capable to simulate traffic on motorways. In spite of the improvements these models can result in local instabilities in the movement of the follower vehicles, especially when there are numerous followers in a convoy [8].…”

Section: Introductionmentioning

confidence: 99%

Parameters of the intelligent driver model in signalized intersections

et al. 2016

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Original scientific paper In the present paper the constant parameters of the (car following) Intelligent Driver Model are calibrated so as to obtain correct flow capacities of a signalized intersection. The calibration is based on measured time-headway parameters of the consecutive vehicles. The measurement data were taken from the work published by Dey et al. The parameters were calibrated partly by numeric calculations and partly by computer simulation, which was developed by the authors of this paper. The simulated environment was a single lane road terminated by a signalized intersection with a queue of stopped cars. As the result of the calibration, the model produced the measured saturated time-headway constant in the stationary flow phase and gave a good approximation of the consecutive time-headways in the initial transient phase. It was found that the calibrated constants of the Intelligent Driver Model considerably differ from the values proposed for modelling traffic on motorways: in a signalized intersection situation higher maximal acceleration and lower safe time gap parameters should be applied. More precisely, the differences are about 30 % in each case. Keywords: Intelligent Driver Model; signalized intersection; traffic simulation Parametri modela inteligentnog vozača u signaliziranim križanjimaIzvorni znanstveni članka U ovom su radu kalibrirani nepromjenjivi parametri Modela inteligentnog vozača (auta koji slijedi) kako bi se dobila točna propusna moć signaliziranog križanja. Kalibriranje se zasniva na izmjerenim parametrima intervala između dvaju vozila u nizu. Podaci mjerenja su uzeti iz rada u izdanju Dey et al. Parametri su kalibrirani djelomično numeričkim kalkulacijama i djelomično računalnom simulacijom, koju su razvili autori ovoga rada. Simulirano okruženje bila je traka koja je završavala signaliziranim križanjem s redom zaustavljenih vozila. Kao rezultat kalibriranja, model je davao izmjerenu konstantu zasićenja između dvaju vozila u nizu u fazi saobraćaja u mirovanju te je dao dobru aproksimaciju vremena između dvaju vozila u nizu u početnoj prolaznoj fazi. Ustanovljeno je da se kalibrirane konstante Modela inteligentnog vozača znatno razlikuju od vrijednosti predloženih za modeliranje prometa na autocestama: kad se radi o signaliziranim križanjima trebalo bi primijeniti parametre većeg maksimalnog ubrzanja i manjeg intervala sigurnog slijeda između dvaju vozila. Točnije, postoje razlike od oko 30 % u svakom slučaju. Ključne riječi:Model inteligentnog vozača; signalizirano križanje; simulacija prometa

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Validation of Microscopic Traffic Models Based on GPS Precise Measurement of Vehicle Dynamics

Cited by 5 publications

References 5 publications

Validation Strategy as a Part of the European Gas Network Protection

Validation Strategy as a Part of the European Gas Network Protection

Dynamic Impact Modeling as a Road Transport Crisis Management Support Tool

Parameters of the intelligent driver model in signalized intersections

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