Paper Info
Title
Reducing the Error Accumulation in Car-Following Models Calibrated With Vehicle Trajectory Data
Abstract
With the development of probe vehicle technologies and the emerging connected vehicle technologies, applications and models using trajectory data for calibration and validation significantly increase. However, the error accumulation issue accompanied by the calibration process has not been fully investigated and addressed. This paper explores the mechanism and countermeasures of the error accumulation problems of car-following models calibrated with microscopic vehicle trajectory data. In this paper, we first derive the error dynamic model based on an acceleration-based generic car-following model formulation. The stability conditions for the error dynamic model are found to be different from the model stability conditions. Therefore, adjusting feasible ranges of model parameters in the car-following model calibration to ensure model stability cannot guarantee the error stability. However, directly enforcing those error stability conditions can be ineffective, particularly when explicit formulations are difficult to obtain. To overcome this issue, we propose several countermeasures that incorporate error accumulation indicators into the error measures used in the calibration. Numerical experiments are conducted to compare the traditional and the proposed error measures through the calibration of five representative car-following models, i.e., General Motors, Bando, Gipps, FREeway SIMulation (FRESIM), and intelligent driver model (IDM) models, using field trajectory data. The results indicate that the weighted location mean absolute error (MAE) and the location MAE with crash rate penalty can achieve the best overall error accumulation performance for all five models. Meanwhile, traditional error measures, velocity MAE, and velocity Theil's U also achieve satisfactory error accumulation performance for FRESIM and IDM models, respectively.
Year
DOI
Venue
2014
10.1109/TITS.2013.2273872
Intelligent Transportation Systems, IEEE Transactions  
Keywords
DocType
Volume
acceleration control,automobiles,calibration,intelligent transportation systems,road accidents,road safety,road traffic control,stability,statistical analysis,trajectory control,Bando,FRESIM,FREeway SIMulation,General Motors,Gipps,IDM models,MAE,acceleration-based generic car-following model formulation,car-following model calibration,crash rate penalty,error accumulation indicators,error accumulation problems,error dynamic model,error measures,error stability,intelligent driver model,microscopic vehicle trajectory data,model parameters,model stability conditions,vehicle technologies,weighted location mean absolute error,Accumulative error,Next-Generation Simulation (NGSIM),car-following models,stability analysis,traffic simulation
Journal
15
Issue
ISSN
Citations 
1
1524-9050
5
PageRank 
References 
Authors
0.48
3
3
Name
Order
Citations
PageRank
Peter J. Jin1535.29
Da Yang251.16
Bin Ran319431.52