01099196

Component

https://doi.org/10.3141/2088-16

http://scholar.google.com...iber&publication_year=2008

The car-following behavior of individual drivers in real city traffic is studied on the basis of (publicly available) trajectory data sets recorded by a vehicle equipped with a radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, the intelligent driver model and the velocity difference model are calibrated by minimizing the deviations between the observed driving dynamics and the simulated trajectory in following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, that is, different measures for the deviations between two trajectories. The obtained errors are between 11% and 29%, which is consistent with typical error ranges obtained in previous studies. It is also found that the calibrated parameter values of the velocity difference model depend strongly on the optimization criterion, whereas the intelligent driver model is more robust. The influence of a reaction time is investigated by applying an explicit delay to the model input. A negligible influence of the reaction time is found and indicates that drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories; this step allows for model validation. The results indicate that intradriver variability rather than interdriver variability accounts for a large part of the calibration errors. The results are used to suggest some criteria toward a benchmarking of car-following models.

01121596

English

pp 148-156

2008

Transportation Research Record: Journal of the Transportation Research Board

9780309126038

Print

Figures (4) ; References (22) ; Tables (2)

Behavior; Calibration; Car following; Data files; Drivers; Errors; Genetic algorithms; Reaction time; Vehicle spacing; Vehicle trajectories; Velocity

Car following models

Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB, ATRI

Jan 29 2008 4:39PM

• Asymptotic Stability and Vehicle Safety in Dynamic Car-Following Platoon
• Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models
• Comparisons of Speed–Spacing Relations Under General Car Following Versus Lane Changing
• Desired Speed Distributions on Two-Lane Highways Under Various Conditions
• Empirical Measurement of Freeway Oscillation Characteristics: An International Comparison
• Empirical Study of Behavioral Theory of Traffic Flow: Analysis of Recurrent Bottleneck
• Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data
• Estimating Individual Speed–Spacing Relationship and Assessing Ability of Newell’s Car-Following Model to Reproduce Trajectories
• Evaluation of Corridor Traffic Management and Planning Strategies That Use Microsimulation: A Case Study
• FASTLANE: New Multiclass First-Order Traffic Flow Model
• From Existing Accident-Free Car-Following Models to Colliding Vehicles: Exploration and Assessment
• Lane Selection Model for Urban Intersections
• Macroscopic Modeling Framework Unifying Kinematic Wave Modeling and Three-Phase Traffic Theory
• Microsimulation Calibration Using Speed–Flow Relationships
• Modeling Driver Behavior as Sequential Risk-Taking Task
• New Behavioral Model for Microscopic Freeway Traffic-Flow Simulation
• On-Ramp Metering Based on Three-Phase Traffic Theory Downstream Off-Ramp and Upstream On-Ramp Bottlenecks
• Oversaturated Freeway Flow Algorithm for Use in Next Generation Simulation
• The Less-Than-Perfect Driver: A Model of Collision-Inclusive Car-Following Behavior
• Use of Microsimulation to Model Day-to-Day Variability of Intersection Performance
• Validity of Trajectory-Based Calibration Approach of Car-Following Models in Presence of Measurement Errors
• What Do Different Traffic Flow Models Mean for System-Optimal Dynamic Traffic Assignment in a Many-to-One Network?