The validation of a microscopic simulation model: a methodological case study

Wu, Jingxian; Brackstone, Mark; McDonald, M.

doi:10.1016/j.trc.2003.05.001

Cited by 73 publications

(33 citation statements)

References 12 publications

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“…Headways have been found to increase with driver age and males are reported to choose shorter headways than females [2]. In addition, drivers aged 59 or more preferred a headway 1.83 s, about 23% more than the normal driver (age range from 23 to 37) [3].…”

Section: Brief Review Of Car-following Modelsmentioning

confidence: 99%

Comparative Evaluation of Microscopic Car-Following Behavior

Panwai

Dia

2005

IEEE Trans. Intell. Transport. Syst.

248

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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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Section: Brief Review Of Car-following Modelsmentioning

confidence: 99%

Comparative Evaluation of Microscopic Car-Following Behavior

Panwai

Dia

2005

IEEE Trans. Intell. Transport. Syst.

248

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“…Wu et al in [32] adopted a concept validation methodology in the comparison of their car-following model output with real data. The concepts used in this paper could also be used to check our data patterns.…”

Section: Data Validity and Driver Propertiesmentioning

confidence: 99%

“…Accordingly, one of the important criteria is to appreciate that the separation at which the vehicle pair stabilizes is not actually constant but oscillates within a certain range due to the misperceptions in the decision process. This may cause overcompensation leading to goal seeking [32]. Fig.…”

Section: Data Validity and Driver Propertiesmentioning

confidence: 99%

“…One common disadvantage of these models is that the regimes or thresholds are solely defined by the knowledge or commonsense of modelers, possibly with bias. Even though later researchers tried to calibrate some psychological thresholds by real data [32], a consolidated method has not been developed to identify the regime classifications based on properties in real data. One possible approach is to use supervised classification methods [31], e.g., Bayesian classification, but it requires detailed prior knowledge in different car-following regimes, e.g., probability distributions of the variables.…”

Section: Classification Of Car-following Regimesmentioning

confidence: 99%

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Behavior Measurement, Analysis, and Regime Classification in Car Following

Andréasson

2007

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly carfollowing and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.

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“…In the model cars are regarded as discrete and interacting particles without overtaking to analyze traffic flow characteristics. So far, a variety of models have been developed, including safety distance model, stimulus-response model and fuzzy logic based model [1][2][3][4][5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

An improved car-following model considering variable safety headway distance

Jia¹,

Du²,

Wu³

2014

phys essays

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Considering high speed following on expressway or highway, an improved car-following model is developed in this paper by introducing variable safety headway distance. Stability analysis of the new model is carried out using the control theory method. Finally, numerical simulations are implemented and the results show good consistency with theoretical study.Reśume: A l'égard du phénomène de car-following , c'est-à -dire la suite de voiture, à grande vitesse sur l'autoroute, cet article présente un modèle car-following basé sur la distance de sé curité minimum de variable . Ensuite, l'analyse de la stabilité du nouveau modèle en utilisant la théorie de la commande est effectué, suivie par un grand nombre d'expérimentations simulé es mises en oeuvre. Enfin, les ré sultats montrent que l'étude théorique du nouveau modèle est correcte.

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The validation of a microscopic simulation model: a methodological case study

Cited by 73 publications

References 12 publications

Comparative Evaluation of Microscopic Car-Following Behavior

Comparative Evaluation of Microscopic Car-Following Behavior

Behavior Measurement, Analysis, and Regime Classification in Car Following

An improved car-following model considering variable safety headway distance

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