Calibrating Car-Following Model Considering Measurement Errors

Shao, Chunli; Liu, Xiaoming; Zhang, Zhiyong

doi:10.1155/2013/890741

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…On the other hand, no matter how advanced the sensors are, the errors cannot all be eliminated. Considering this, Shao et al proposed a two-step calibration algorithm with consideration of the detecting error of the data set and verified the algorithm employing the Van-Aerde model along with the data set collected from field experiments organized by themselves [123]. In the same work, Punzo et al pointed out that the large length of the trajectory in the data set can improve the performance of models calibrated with this data set [122].…”

Section: Data Set Used In Calibratingmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

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The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

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Section: Data Set Used In Calibratingmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Rakha et al [2] developed an automated data processing software based on US highway data and the Van Aerde model. Shao et al [3] calibrated the Van Aerde model by considering the impact of measurement errors on estimation accuracy. Traffic flow data has strong time correlation [4] , and traditional traffic flow models usually only consider parameters such as free flow speed and capacity, ignoring the changing trends of traffic flow at different times.…”

Section: Introductionmentioning

confidence: 99%