Ring-Road Traffic Simulation

Cars drive around a circular track using the Intelligent Driver Model (IDM) of Martin Treiber and co-authors. Even without obstacles, small perturbations grow into stop-and-go waves — the classic Sugiyama experiment. Enable the Driver noise slider to add stochastic acceleration error and compare three stochastic car-following models: white noise (B-IDM), a Gaussian-process kernel from Zhang & Sun (2024), MA-IDM, and an autoregressive process from Zhang, Wang & Sun (2024), dynamic-regression IDM.

Average speed (m/s)

Flow (cars / hr, region)

Density (cars / km, region)

Fundamental diagram (flow vs density, region sub-bins)

Time–space diagram (time →, position ↑; dark = jam)

Author & contact

Chengyuan Zhang
Department of Civil Engineering
McGill University, Montreal, Canada
enzozcy@gmail.com
🌐Homepage ⌗GitHub 🎓Google Scholar iDORCID inLinkedIn ★Source

Citation

If this simulator is useful in your work, please cite the underlying papers:

  1. Zhang, C., & Sun, L. (2024). Bayesian Calibration of the Intelligent Driver Model. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2024.3354102 [arXiv:2210.03571]
  2. Zhang, C., Wang, W., & Sun, L. (2024). Calibrating Car-Following Models via Bayesian Dynamic Regression. Transportation Research Part C: Emerging Technologies, 104719. doi:10.1016/j.trc.2024.104719 [arXiv:2307.03340]
BibTeX 
@article{zhang2024maidm,
  title   = {Bayesian Calibration of the Intelligent Driver Model},
  author  = {Zhang, Chengyuan and Sun, Lijun},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  year    = {2024},
  doi     = {10.1109/TITS.2024.3354102}
}

@article{zhang2024dynamicidm,
  title   = {Calibrating Car-Following Models via Bayesian Dynamic Regression},
  author  = {Zhang, Chengyuan and Wang, Wenshuo and Sun, Lijun},
  journal = {Transportation Research Part C: Emerging Technologies},
  year    = {2024},
  pages   = {104719},
  doi     = {10.1016/j.trc.2024.104719}
}