Implementation of dynamic origin-destination demand estimation
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PCA_LDA_results
P_matrix
Q_vector
R_matrix
X_vector
.gitattributes
DPFE-v0.1.ipynb
LICENSE
README.md
base.py
cluster_info.pickle
graph.pickle
observe_index_N.npy
od_list.pickle
pfe.py

README.md

Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

Implemented by Wei Ma, advised by Sean Qian, Civil and environmental engineering, Carnegie Mellon University.

Requirements

  • Python 2.7.13
  • PyTorch 0.2.0_3
  • Numpy 1.13.3
  • Scipy 0.19.1
  • NetworkX 1.11
  • pickle
  • joblib 0.11
  • pandas 0.18.1

Instructions

Please clone the whole repo, and run DPFE-v0.1.ipynb using jupyter notebook.

File specifications

  • P_matrix: store the route choice portion matrices
  • Q_vector: store the estimated dynamic OD
  • R_matrix: store the DAR matrices
  • X_vector: store the observed link flow
  • observe_index_N.npy: observed link indices
  • link_count_data.pickle: flow data
  • link_spd_data.pickle: speed data
  • od_list.pickle: OD information
  • graph.pickle: graph information
  • cluster_info.pickle: traffic scenario information
  • base.py: data processing, DAR matrix construction, P matrix construction
  • pfe.py: stochastic projected gradient descent
  • DPFE-v0.1.ipynb: main script, start from here

Paper

Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

Data

Since the traffic speed data (link_spd_data.pickle) and count data (link_count_data.pickle) are under the non-discloure agreement, please contact the authors to obtain the data.

For any questions, please contact Lemma171@gmail.com