Awesome

Academic drawing

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

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

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

mu_curve10

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

overfitting_ms30_r5 overfitting_ms30_r10

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

heat_map10

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

factor2 factor3

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

rmse_curve

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

rmse_curve10

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

time_series_speed1

time_series_speed2

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

speed_curve

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

nyc_data_completeness

Most of these examples are from our publications:

Xinyu Chen, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model. Transportation Research Part C: Emerging Technologies, 104: 66-77. [preprint] [doi] [slide] [data] [Matlab code]
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. [preprint] [doi] [data] [Matlab code] [Python code]

Please consider citing our papers if you find these codes help your research.