Toward Benchmarking of Microscopic Traffic Flow Models

Brockfeld, Elmar; Kühne, Reinhart; Skabardonis, Alexander; Wagner, Péter

doi:10.3141/1852-16

Cited by 88 publications

(51 citation statements)

References 13 publications

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“…The additional errors in comparison to the calibration are -apart from singular cases of "overfitting" -mainly in the area of 3 to 5 percentage points. The results after the calibration and the validation agree with results that have been obtained before with a completely different data set taking the travel times on road segments instead of headways for the error measurement [14]. In these studies about 15 % to 27 % were found to be the minimum calibration error and additional validation-errors were found to be about 2 to 5 percentage points.…”

Section: Discussionsupporting

confidence: 80%

Calibration and Validation of Microscopic Traffic Flow Models

Brockfeld

Kühne

Wagner

2004

Transportation Research Record: Journal of the Transportation R

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181

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Summary. The aim of this paper is to present recent progress in calibrating ten microscopic traffic flow models. The models have been tested using data collected via DGPS-equipped cars (Differential Global Positioning System) on a test track in Japan. To calibrate the models, the data of a leading car are fed into the model under consideration and the model is used to compute the headway time series of the following car. The deviations between the measured and the simulated headways are then used to calibrate and validate the models. The calibration results agree with earlier studies as there are errors of 12 % to 17 % for all models and no model can be denoted to be the best. The differences between individual drivers are larger than the differences between different models. The validation process leads to errors from 17 % to 22 %. But for special data sets with validation errors up to 60 % the calibration process has reached what is known as "overfitting": because of the adaptation to a particular situation, the models are not capable of generalizing to other situations.

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Section: Discussionsupporting

confidence: 80%

Calibration and Validation of Microscopic Traffic Flow Models

Brockfeld

Kühne

Wagner

2004

Transportation Research Record: Journal of the Transportation R

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181

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“…However, the differences or similarities between the published version of the Fritzsche model and the version used in PARAMICS are unknown [20].…”

Section: B Paramicsmentioning

confidence: 99%

Comparative Evaluation of Microscopic Car-Following Behavior

Panwai

Dia

2005

IEEE Trans. Intell. Transport. Syst.

258

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Abstract-Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Stadten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative "drift and goal-seeking behavior" test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

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“…So, in a final step, it is usually necessary to apply a model with a fitted set of parameters to a different scenario and see how well it fits therewithout adapting the parameters once more. This final step is called validation, and usually the validation results are another 5 per cent worse than the calibration results (Brockfeld et al 2003(Brockfeld et al , 2004(Brockfeld et al , 2005. Within this work, only calibration results will be provided, since a direct comparison between fluid-dynamical and microscopic models is undertaken.…”

Section: (D) Calibration/validation: Optimization Issuesmentioning

confidence: 99%

“…This is not as straightforward as using the speeds since it has to be ensured that no more than the measured flow q sim,out (t) ≤ q out (t) leaves the study area. This can again be reached by an appropriately set speed limit, or by changing the preferred headway of the leading vehicle that slows it down, or, finally, by using a virtual traffic light (Brockfeld et al 2003) that enforces the condition q sim,out (t) ≤ q out (t). However, this causes a strong disturbance in the traffic flow and should be done only if nothing else works, e.g.…”

Section: (C) Boundary Conditionsmentioning

confidence: 99%

“…error with M being the number of data points. Other measures of performance (Brockfeld et al 2003(Brockfeld et al , 2005Toledo et al 2007) might be used and may influence the outcome of the calibration task, see Brockfeld et al (2004), Toledo et al (2004), Hoogendoorn & Ossen (2005) and Ossen & Hoogendoorn (2007). Note that for the models considered here, a homogeneity assumption has been made: all vehicles for the microscopic models share the same set of parameters, and the same is true for the fluid-dynamical models: all parameters are kept constant over the simulation time (one day) and the simulation area.…”

Section: (D) Calibration/validation: Optimization Issuesmentioning

confidence: 99%

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Fluid-dynamical and microscopic description of traffic flow: a data-driven comparison

Wagner

2010

Phil. Trans. R. Soc. A.

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Much work has been done to compare traffic-flow models with reality; so far, this has been done separately for microscopic, as well as for fluid-dynamical, models of traffic flow. This paper compares directly the performance of both types of models to real data. The results indicate that microscopic models, on average, seem to have a tiny advantage over fluid-dynamical models; however, one may admit that for most applications, the differences between the two are small.Furthermore, the relaxation times of the fluid-dynamical models turns out to be fairly small, of the order of 2 s, and are comparable with the results for the microscopic models. This indicates that the second-order terms are weak; however, the calibration results indicate that the speed equation is, in fact, important and improves the calibration results of the models.

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Toward Benchmarking of Microscopic Traffic Flow Models

Cited by 88 publications

References 13 publications

Calibration and Validation of Microscopic Traffic Flow Models

Calibration and Validation of Microscopic Traffic Flow Models

Comparative Evaluation of Microscopic Car-Following Behavior

Fluid-dynamical and microscopic description of traffic flow: a data-driven comparison

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