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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