Neural network car-following model
In this code a feed-forward neural network or a recurrent neural network is trained on data from a traffic jam experiment conducted by Sugiyama et al. (2008) (http://iopscience.iop.org/article/10.1088/1367-2630/10/3/033001/meta). In this experiment cars drove in a circuit of 230m. Two experiments were done (case 1 and 2) with 22 or 23 cars, respectively. With the script "preprocessing.py" the headway, velocity and acceleration is calculated for each car. The neural network is given the task to predict the acceleration for a car of the subsequent data point based on the car's headway and the velocity (and if wanted from cars ahead). The training is done in the script "train.py". In the script "run.py" the trained neural network is used to simulate cars driving on a circuit.
Requirements
Keras
Numpy
matplotlib
pandas
Data
The position data of the cars in the experiment can be downloaded here: http://iopscience.iop.org/article/10.1088/1367-2630/10/3/033001/meta
Remark
Up until now, the trained neural network often produces crashes. Mostly, because cars start to drive backwards. If just positive velocities are allowed (see model.integration_procedure), less crashes occur.