Calibrating car‐following parameters for snowy road conditions in the microscopic traffic simulator VISSIM

Asamer, Johannes; Zuylen, Henk J van; Heilmann, Bernhard

doi:10.1049/iet-its.2011.0193

Cited by 21 publications

(12 citation statements)

References 10 publications

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“…Zhao et al also observed that the maximum observed acceleration decreases from 4.36 to 2.50 m/s 2 under inclement weather. Finally, Asamer et al showed that the desired acceleration can be reduced from 10% to 100% by adverse conditions, and in their experiments, acceleration showed a reduction to 71% of original acceleration settings.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…In our approach, in contrast to pruning the SPL, we delimit the possible configurations to be selected by defining Object Constraint Language (OCL) constraints over the variability model. These OCL constraints set the most appropriate parameter/model for each context, as the Asamer approach does for the car‐following parameters. This makes the generation of the configuration in our approach straightforward due to the tree constraints and cross‐tree constraints of the variability model.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…Several studies have investigated the influence of weather conditions on driving behaviour: from rain, [19][20][21][22][23] to snow, 21,24,25 fog, 20,24 or even temperature and wind. 24 As in this paper we have focused on rainy adverse conditions, we investigated the influence of weather conditions on the behavioural parameter considered: time headway (Th) and acceleration (acc).…”

Section: Effects Of Weather Conditions On Parametersmentioning

confidence: 99%

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Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Horcas

Monteil

Bouroche

et al. 2017

J Software Evolu Process

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Human drivers naturally adapt their behaviour depending on the traffic conditions, such as the current weather and road type. Autonomous vehicles need to do the same, in a way that is both safe and efficient in traffic composed of both conventional and autonomous vehicles. In this paper, we demonstrate the applicability of a reconfigurable vehicle controller agent for autonomous vehicles that adapts the parameters of a used car‐following model at runtime, so as to maintain a high degree of traffic quality (efficiency and safety) under different weather conditions. We follow a dynamic software product line approach to model the variability of the car‐following model parameters, context changes and traffic quality, and generate specific configurations for each particular context. Under realistic conditions, autonomous vehicles have only a very local knowledge of other vehicles' variables. We investigate a distributed model predictive controller agent for autonomous vehicles to estimate their behavioural parameters at runtime, based on their available knowledge of the system. We show that autonomous vehicles with the proposed reconfigurable controller agent lead to behaviour similar to that achieved by human drivers, depending on the context.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Section: Literature Review and Related Workmentioning

confidence: 99%

Section: Effects Of Weather Conditions On Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Horcas

Monteil

Bouroche

et al. 2017

J Software Evolu Process

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show abstract

“…The OAT measures are based on the estimation of partial derivatives to assess how uncertainty in one factor would affect the model output by fixing the other factors to their nominal values. The OAT approach has been applied to identify the important parameters in VISSIM and to get insight on the meaning of the values of parameters resulting from the calibration of the intelligent driving and the velocity difference models (Lownes and Machemehl, 2006;Kesting and Treiber, 2008;Mathew and Radhakrishnan, 2010;Asamer et al, 2013). The drawbacks of this method include the locality, i.e.…”

Section: Introductionmentioning

confidence: 99%

A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models

Zhong

Sumalee

et al. 2016

Transportation Research Part C: Emerging Technologies

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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.A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models AbstractCar following modeling framework seeks for a more realistic representation of car following behavior in complex driving situations to improve traffic safety and to better understand several puzzling traffic flow phenomena, such as stop-and-go oscillations. Calibration and validation techniques pave the way towards the descriptive power of car-following models and their applicability for analyzing traffic flow. However, calibrating these models is never a trivial task. This is caused by the fact that some parameters, such as reaction time, are generally not directly observable from traffic data. On the other hand, traffic data might be subject to various errors and noises. This contribution puts forward a Cross-Entropy Method (CEM) based approach to identify parameters of deterministic car-following models under noisy data by formulating it as a stochastic optimization problem. This approach allows for statistical analysis of the parameter estimations. Another challenge arising in the calibration of car following models concerns the selection of the most important parameters. This paper introduces a relative entropy based Probabilistic Sensitivity Analysis (PSA) algorithm to identify the important parameters so as to reduce the complexity, data requirement and computational effort of the calibration process. Since the CEM and the PSA are based on the Kullback-Leibler (K-L) distance, they can be simultaneously integrated into a unified framework to further reduce the computational burden. The proposed framework is applied to calibrate the intelligent driving model using vehicle trajectories data from the NGSIM project. Results confirm the great potential of this approach.

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“…The optimization task involves comparing and minimization differences of selected indicators, e.g., travel time and queuing length [4], delays [6], travel time distribution [7], saturation flow rates [8] and emission [9], between the calibration model and the ones counted and measured in local traffic network.…”

Section: Introductionmentioning

confidence: 99%

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

Qin¹,

Dong²,

Zhang³

et al. 2016

International Conferences on Distributed Multimedia Systems

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Abstract-Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown.

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Calibrating car‐following parameters for snowy road conditions in the microscopic traffic simulator VISSIM

Cited by 21 publications

References 10 publications

Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

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