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Improving the Intelligent Driver Model by Incorporating Vehicle Dynamics: Microscopic Calibration and Macroscopic Validation

Dominik Salles, Steve Oswald, Hans-Christian Reuss

TL;DR

This work addresses the challenge of accurately calibrating microscopic car-following models to reproduce real urban driving behavior and its macroscopic consequences. It extends the Intelligent Driver Model with vehicle-dynamics-based equations (EIDM) and calibrates it against drone-derived trajectories using differential evolution, demonstrating superior microscopic fit and the emergence of realistic macroscopic phenomena such as capacity drop and stop-and-go waves. The results show that EIDM outperforms the standard IDM and Krauss models, and that incorporating dynamics yields additional gains in fidelity. The study provides a practical calibration framework and open-source tools, enabling more reliable city-scale traffic simulations for evaluating infrastructure and automated-vehicle technology scenarios.

Abstract

Microscopic traffic simulations are used to evaluate the impact of infrastructure modifications and evolving vehicle technologies, such as connected and automated driving. Simulated vehicles are controlled via car-following, lane-changing and junction models, which are designed to imitate human driving behavior. However, physics-based car-following models (CFMs) cannot fully replicate measured vehicle trajectories. Therefore, we present model extensions for the Intelligent Driver Model (IDM), of which some are already included in the Extended Intelligent Driver Model (EIDM), to improve calibration and validation results. They consist of equations based on vehicle dynamics and drive off procedures. In addition, parameter selection plays a decisive role. Thus, we introduce a framework to calibrate CFMs using drone data captured at a signalized intersection in Stuttgart, Germany. We compare the calibration error of the Krauss Model with the IDM and EIDM. In this setup, the EIDM achieves a 17.78 % lower mean error than the IDM, based on the distance difference between real world and simulated vehicles. Adding vehicle dynamics equations to the EIDM further improves the results by an additional 18.97 %. The calibrated vehicle-driver combinations are then investigated by simulating the traffic in three different scenarios: at the original intersection, in a closed loop and in a stop-and-go wave. The data shows that the improved calibration process of individual vehicles, openly available at https://www.github.com/stepeos/pycarmodel_calibration, also provides more accurate macroscopic results.

Improving the Intelligent Driver Model by Incorporating Vehicle Dynamics: Microscopic Calibration and Macroscopic Validation

TL;DR

This work addresses the challenge of accurately calibrating microscopic car-following models to reproduce real urban driving behavior and its macroscopic consequences. It extends the Intelligent Driver Model with vehicle-dynamics-based equations (EIDM) and calibrates it against drone-derived trajectories using differential evolution, demonstrating superior microscopic fit and the emergence of realistic macroscopic phenomena such as capacity drop and stop-and-go waves. The results show that EIDM outperforms the standard IDM and Krauss models, and that incorporating dynamics yields additional gains in fidelity. The study provides a practical calibration framework and open-source tools, enabling more reliable city-scale traffic simulations for evaluating infrastructure and automated-vehicle technology scenarios.

Abstract

Microscopic traffic simulations are used to evaluate the impact of infrastructure modifications and evolving vehicle technologies, such as connected and automated driving. Simulated vehicles are controlled via car-following, lane-changing and junction models, which are designed to imitate human driving behavior. However, physics-based car-following models (CFMs) cannot fully replicate measured vehicle trajectories. Therefore, we present model extensions for the Intelligent Driver Model (IDM), of which some are already included in the Extended Intelligent Driver Model (EIDM), to improve calibration and validation results. They consist of equations based on vehicle dynamics and drive off procedures. In addition, parameter selection plays a decisive role. Thus, we introduce a framework to calibrate CFMs using drone data captured at a signalized intersection in Stuttgart, Germany. We compare the calibration error of the Krauss Model with the IDM and EIDM. In this setup, the EIDM achieves a 17.78 % lower mean error than the IDM, based on the distance difference between real world and simulated vehicles. Adding vehicle dynamics equations to the EIDM further improves the results by an additional 18.97 %. The calibrated vehicle-driver combinations are then investigated by simulating the traffic in three different scenarios: at the original intersection, in a closed loop and in a stop-and-go wave. The data shows that the improved calibration process of individual vehicles, openly available at https://www.github.com/stepeos/pycarmodel_calibration, also provides more accurate macroscopic results.
Paper Structure (8 sections, 3 equations, 6 figures, 1 table)

This paper contains 8 sections, 3 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Model Calibration
  4. Model Validation
  5. Original Intersection Simulation
  6. Closed Loop Simulation
  7. Stop-and-go Simulation
  8. Conclusion

Figures (6)

  • Figure 1: Results of the sensitivity analysis with the EIDM parameters
  • Figure 2: RMSE between the simulated trajectory with the calibrated model and the corresponding real world trajectory
  • Figure 3: Average acceleration curves of the first nine vehicles queued at the signalized intersection
  • Figure 4: Real data and model results for time headway, velocity and acceleration of the queued vehicles after crossing the stop line
  • Figure 5: Flow-density and speed-density diagrams of the circular route simulations
  • ...and 1 more figures