Beyond behaviour change: investigating alternative explanations for shorter time headways when human drivers follow automated vehicles

This study is published in the journal "Transportation Research Part C: Emerging Technologies" with gold open access, available at https://doi.org/10.1016/j.trc.2024.104673.

Highlights

• Observation biases are inherent in data collected by automated vehicles (AVs).
• 3 explanations for shorter headways of human drivers following AVs are validated.
• Systematic differences in data collection and AV driving play remarkable roles.
• Driving homogeneity and stability contribute to reliable reduction in time headway.
• Behavioural insights from non-behavioural data require scrutiny.

Package requirements

jupyter notebook, zarr, numpy, pandas, pytables, tqdm, matplotlib, scipy, joblib, pytorch

In order to repeat the experiments:

Step 0. Preparation

Create a conda environment for repeating the experiments. Install the required packages as listed above.

Clone this repository, then either 1) create/define a folder for data saving and copy the subfolders in "Data"; or 2) use the folder "Data" directly.

Step 1. Download and save data

Download the trajectory data of Lyft from https://github.com/RomainLITUD/Car-Following-Dataset-HV-vs-AV and save them in the folder "Data/InputData/Lyft/"; download processed data of Waymo from https://data.mendeley.com/datasets/wfn2c3437n/2 and save it (all_seg_paired_cf_trj_final_with_large_vehicle.csv) in the folder "Data/InputData/Waymo/".

Step 2. Standardise data format (of Waymo and Lyft)

Run data_format_standardization.py in the current parent folder first to preprocess the trajectories.

Step 3. Regime categorisation

In the folder "Car-following regime categorisation", use regime categorisation.ipynb to categorise car-following regimes in Waymo and Lyft datasets.

Step 4. IDM calibration and simulation

In the folder "Car-following modelling and simulation", run idm_data_selection.py to select car-following pairs that cover sufficient regimes for Intelligent Driver Modelling.

Then in the sub-folder "Car-following modelling and simulation/IDM calibration", run idm_calibration.py to calibrate IDMs and run loss_computation.py to calculate calibration loss. Further in the sub-folder "IDM calibration/Appendix", we offer the calibration of the other two car-following models, Newell and Gipps, to enhance the robustness of the results.

Finally, in the sub-folder "Car-following modelling and simulation/Controlled simulation", run cross_follow_leader.py and cross_follow_follower.py to simulate the designed experiments.

Step 5. Leading vehicle classification

In the folder "Leading vehicle driving classification", use dataset_separation.py to separate Lyft data into train, val, and test sets, and then use classifier_lstm.ipynb to train the LSTM classifier, validate the trained model, and save test results.

* In doing regime categorisation, we resued the code from https://github.com/slaypni/fastdtw to apply fast dynamic time warping. This is also indicated in the folder.

* We have run the IDM calibration in Linux with 15 CPUs. To be run on other OSs may need adjustments regarding the number of cores/workers for parallel processing.

Citation

@article{Jiao2024,
  title = {Beyond behavioural change: Investigating alternative explanations for shorter time headways when human drivers follow automated vehicles},
  volume = {164},
  doi = {10.1016/j.trc.2024.104673},
  journal = {Transportation Research Part C: Emerging Technologies},
  author = {Jiao,  Yiru and Li,  Guopeng and Calvert,  Simeon C. and van Cranenburgh,  Sander and van Lint,  Hans},
  year = {2024},
  pages = {104673}
}