This code uses Computational Graph(CG) and Neural Network(NN) to solve the five-layer Traffic Demand Estimation(TDE) in Sioux Falls(SF) network. A 5TCG (five-layer traffic computational graph) model is established, including traffic generation flow > traffic OD(Origin-Destination) flow > traffic path flow > traffic link flow > traffic intersection flow.
It also includes comparison of models accuracy with 4TCG model which was established by Wu Xin[1] and developed a 10-fold cross-validation framework for CG.
[1] Wu, X., Guo, J., Xian, K., & Zhou, X. (2018b). Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph. Transportation Research Part C-Emerging Technologies, 96, 321-346. (https://www.sciencedirect.com/science/article/abs/pii/S0968090X18306685)
Python & Anaconda & Tensorflow
Py 3.8 & tf 2.2