Car-Following Behavior Analysis from Microscopic Trajectory Data

Ossen, Saskia; Hoogendoorn, Serge P.

doi:10.1177/0361198105193400102

Cited by 63 publications

(52 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This former implements calibration with respect to macroscopic traffic data, for example, flow-density data for the specific region, or estimates origin-destination matrices [3]. The latter treats vehicles as an individual entities and uses microscopic trajectory data [5,9]. The goal is to determine the optimal model parameters that better reproduce vehicle dynamics (acceleration on free-road, deceleration process) and drivers' behaivior (distance between vehicles in a steady-state flow).…”

Section: Model Calibrationmentioning

confidence: 99%

Traffic and Granular Flow '13

Flow¹,

Chraibi²,

Boltes³

et al. 2015

View full text Add to dashboard Cite

The Intelligent Driver Model (IDM) is studied and several drawbacks with respect to driving simulators are defined. We present two modifications of the IDM. The first one gives any predefined distance to the leading vehicle in a steady state. The second modification is a combination of the first one and the optimal velocity model. It takes into account driver's reaction time explicitly and is described by delay differential equation. This model always results in realistic vehicles accelerations what allows simulating real traffic collisions.Necessary and sufficient conditions are obtained, that guarantee a non-oscillating solution near the equilibrium for the vehicle platoon. We suggest the calibrating framework based on a numerical solution of the constrained optimization problem. Nonlinear constraints are generated by the numerical integration scheme. The suggested procedure incorporates the local stability conditions obtained and takes into account vehicle dynamics, drivers' behavior and weather conditions.

show abstract

Section: Model Calibrationmentioning

confidence: 99%

Traffic and Granular Flow '13

Flow¹,

Chraibi²,

Boltes³

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In model (6), is denoted as = 2 V / 2 . For the case of = 1, the optimization function of (6) can be rewritten as…”

Section: Maximum Likelihoodmentioning

confidence: 99%

“…It should be noted that is supposed to be known in model (6) or (15). Hence, the value of should be determined before calibrating the model.…”

Section: Parameter Estimatementioning

confidence: 99%

“…Therefore, the models have become widely preferred for applications in traffic and safety engineering, for example, for capacity analysis, traffic simulation, network analysis, and Advance Vehicle Control [1][2][3]. Especially, as microscopic traffic data (such as trajectory data and floating car data) have become more available, the problem of calibrating car-following traffic flow models with real microscopic data has raised some interest in the literatures [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibrating Car-Following Model Considering Measurement Errors

Shao

Liu

Zhang

2013

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Car-following model has important applications in traffic and safety engineering. To enhance the accuracy of model in predicting behavior of individual driver, considerable studies strive to improve the model calibration technologies. However, microscopic carfollowing models are generally calibrated by using macroscopic traffic data ignoring measurement errors-in-variables that leads to unreliable and erroneous conclusions. This paper aims to develop a technology to calibrate the well-known Van Aerde model. Particularly, the effect of measurement errors-in-variables on the accuracy of estimate is considered. In order to complete calibration of the model using microscopic data, a new parameter estimate method named two-step approach is proposed. The result shows that the modified Van Aerde model to a certain extent is more reliable than the generic model.

show abstract

“…At the microscopic level, while pair longitudinal interactions are now quite well-known [1,2,3,4,5,6,7], there is still much to learn about lateral interactions [8,9,10], and also about collective effects -or correlations between vehicles, to state it otherwise [11,12,13,14,15]. This knowledge would be useful though, both in the perspective of modelling, and of security improvement.…”

Section: Introductionmentioning

confidence: 99%

Experimental study of short-range interactions in vehicular traffic

Appert-Rolland

2009

Phys. Rev. E

View full text Add to dashboard Cite

Single vehicle data obtained from magnetic loops on an expressway allowed us to measure velocity correlations and velocity differences between non-neighbor vehicles on the same lane, showing some strong correlations even for platoons of 7 or 8 vehicles. The corresponding time headway distribution for non-neighbor vehicles is also presented. We were also able to get some informations about interlane structures, through crossed time headway distributions. It should be noticed that these last results on crossed time headways are meaningful only because the subset of data we used corresponds to a unique stationary traffic state -and still contains a large amount of data, with a large fraction of short time headways. The link with passing maneuvers is discussed.

show abstract

Car-Following Behavior Analysis from Microscopic Trajectory Data

Cited by 63 publications

References 8 publications

Traffic and Granular Flow '13

Traffic and Granular Flow '13

Calibrating Car-Following Model Considering Measurement Errors

Experimental study of short-range interactions in vehicular traffic

Contact Info

Product

Resources

About