Calibrating Car-Following Models by Using Trajectory Data

By Arne Kesting, Martin Treiber. 2008

363 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/b5da40d29be42a729cfd5f9c3f28f32d7a1b9d69

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In Automotive Engineering

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Citations in the corpus

Incorporating human-factors in car-following models : a review of recent developments and research needs (2014, methodology): “Discussions on calibrating CF models are scattered in the literature (e.g., Brockfeld et al., 2004; Kesting and Treiber, 2008; Ossen and Hoogendoorn, 2008; Hoogendoorn and Hoogendoorn, 2010), however, guidance on the systematic and rigorous calibration and validation of traffic flow models is still…”
Microscopic Calibration and Validation of Car-Following Models – A Systematic Approach (2013, background): “, 6, 7], and ∗Corresponding author. Email addresses: treiber@vwi.tu-dresden.de(Martin Treiber), mail@akesting.de(Arne Kesting) 2 M. Treiber, A. Kesting / ISTTT Procedia 00 (2018) 1–18 trajectory data [8, 9, 10]. Trajectory data such as these of the NGSIM project [11, 12] contain the dynamics of the ”
Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data (2008, result): “In contrast, there are very few investigations of the data with respect to topics where velocities and accelerations play a significant role such as testing or calibrating car-following models [ 14 ] or lane-changing models, or estimating fuel consumption [15].”
Speed or spacing? Cumulative variables, and convolution of model errors and time in traffic flow models validation and calibration (2016, background): “…to speed and provided an intuitive explanation ✓ Ossen and Hoogendoorn (2007), showed that calibrating against speed some of the Gipps’ model parameters cannot be estimated ✓ Kesting and Treiber (2008) also suggest the use of spacing and compare error measures (see also Hamdar et al., 2015).”