zan00789/academic-drawing

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

Academic drawing

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

Contents

• Usage
• Our examples
• Our publications

Usage

It is not necessary to open each file in this repository because you can follow this readme document to find your needs.

Our examples

• Download
  • mu10.mat
  • mu_curve10.m

and evaluate these in Matlab in a folder, then, you will see the following picture:

• Download
  • BCPF_fiber_rmselb_m30_r5.csv
  • BCPF_fiber_rmselb_m30_r10.csv
  • overfitting.m

and evaluate these in Matlab in a folder, then, you will see the following pictures:

• Download
  • bias10.mat
  • heat_map10.m

and evaluate these in Matlab in a folder, then, you will see the following picture:

• Download
  • rmse.mat
  • rmse_curves.m

and evaluate these in Matlab in a folder, then, you will see the following picture:

• Download
  • rmse10.mat
  • rmse_curve10.m

and evaluate these in Matlab in a folder, then, you will see the following picture:

• Download
  • road1_fiber_ms50_r10.csv
  • time_series_speed1.py
  • time_series_speed2.py

and evaluate these in Python in a folder, then, you will see the following pictures:

• Download
  • road1_fiber_ms50_r10.csv
  • speed_curve.m

and evaluate these in Matlab in a folder, then, you will see the following picture:

Our publications

• Xinyu Chen, Zhaocheng He, Jiawei Wang, 2018. Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transportation Research Part C: Emerging Technologies, 86: 59-77.

• Xinyu Chen, Zhaocheng He, Lijun Sun, 2019. A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84. [Matlab code]

Please consider citing our papers if they help your research.