Exploring the variance contributions of correlated model parameters: A sampling-based approach and its application in traffic simulation models

Ge, Qiao; Menéndez, Mónica

doi:10.1016/j.apm.2021.04.012

Cited by 4 publications

(5 citation statements)

References 57 publications

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“…There is clear potential for future research extending this approach in a couple of areas. Ge and Menendez [41] have laid out a methodology for handling correlated inputs, and it would be interesting to investigate the difference between results when considering correlated input parameters. In the authors' own work, the takeaways from this paper will be used to guide stochastic simulations for traffic signal optimization.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…As both the IDM model and SUMO do not allow for intra-driver variability, it was consequently left out of this simulation. Furthermore, the correlation of CF-model parameters in the NGSIM dataset has been demonstrated [65], with Ge and Menendez [41] identifying potential issues arising from the assumption of independence. They proposed a sensitivity analysis method that supports correlated input parameters.…”

Section: Car Following Model Specificsmentioning

confidence: 98%

“…At this point, the reader would do well to ask the question "variance of what?". In commonly cited literature, the SA is typically done on calibration-specific MoPs [12], [38], [40], [41]. Inline with these prior papers, each model evaluation's ability to pass calibration (described in Section III-C) is assessed in this work and the sensitivity of calibration error is presented.…”

Section: E Exploration Of Variancementioning

confidence: 99%

“…The same observation was made in the formation of this study. In prior works performing SAs on the IDM carfollowing model, it is common to see the lower bound for a less than 0.5 m/sec 2 , with the upper bound of τ reaching 5 sec [25], [41], [63]. These works did not use a traffic micro-simulation software, but instead were investigating a microscopic traffic flow model, which is presented only in mathematical equations.…”

Section: A Samentioning

confidence: 99%

See 3 more Smart Citations

A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics

Schrader,

Bittle

2024

IEEE Trans. Intell. Transport. Syst.

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Traffic microsimulation is regularly used to develop traffic signal control (TSC) algorithms, yet moving from simulation to the field requires confidence in the results and mature understanding of the uncertainty propagation. This work aims to understand the under-reported relationship between simulation inputs and the variance in output metrics commonly used for signal control optimization. Specifically, the impact that fleet composition, intelligent driver model, lane-changing model, and the corresponding inter-driver distribution of those parameters have on delay, fuel consumption, and travel time were ranked using Sobol global sensitivity analysis. The results are presented from 98,304 total simulation evaluations of a volume-calibrated three-intersection SUMO model. Acceleration, deceleration, and headway parameters in the intelligent driver model were shown to be important for fuel consumption, delay and travel time, in addition to the adherence of the drivers to the speed limit, the variance in preferred speed between drivers, and impatience. The results also unsurprisingly show the overwhelming importance of fleet composition for fuel consumption. Additionally, the work presents car-following and lane-change model parameter bounds that result in realistic simulation. The results from this study point to the critical factors that must be determined for any individual region of study and can be used to guide for realworld data collection to support additional calibration efforts. In the future, after calibration of key parameters, the overall uncertainty in network-wide performance metrics can be reduced and provide increased confidence to conclusions drawn from microsimulation studies.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Car Following Model Specificsmentioning

confidence: 98%

Section: E Exploration Of Variancementioning

confidence: 99%

Section: A Samentioning

confidence: 99%

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A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics

Schrader,

Bittle

2024

IEEE Trans. Intell. Transport. Syst.

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“…The correlation of parameters in the simulation of traffic flow has garnered attention in the literature, as it has been shown to impact the realism of simulation results [30], [31]. To create a correlated distribution from the radar data, only vehicles with deceleration, acceleration, and headway less than 5 seconds are considered.…”

Section: Vehicle Distribution Creationmentioning

confidence: 99%

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar

Schrader,

Al Abdraboh,

Bittle

2023

SUMO Conf Proc

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In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

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Using deep generative adversarial network to explore novel airfoil designs for vertical-axis wind turbines

Santos

PANTALEAO

Salviano

2023

Energy Conversion and Management

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Exploring the variance contributions of correlated model parameters: A sampling-based approach and its application in traffic simulation models

Cited by 4 publications

References 57 publications

A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics

A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar

Using deep generative adversarial network to explore novel airfoil designs for vertical-axis wind turbines

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