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Table 2 JS divergences of the distributions of the CPC scores. For each model, we report the JS divergence between mixed set and test set (column \(JS_{m}\)) and the JS divergence between synthetic set and test set (column \(JS_{s}\)). The last four \(\Delta _{x,Z}\)-like columns represent the improvement of MoGAN compared to the Gravity model on the mixed and the synthetic sets (columns \(\Delta _{m,G}\) and \(\Delta _{s,G}\)) and the improvement of MoGAN compared to the Radiation model on the mixed and synthetic sets (columns \(\Delta _{m,R}\) and \(\Delta _{s,R}\))

From: Generating mobility networks with generative adversarial networks

Data

MoGAN

Gravity

Radiation

Rel. Improvement

\(JS_{m}\)

\(JS_{s}\)

\(JS_{m}\)

\(JS_{s}\)

\(JS_{m}\)

\(JS_{s}\)

\(\Delta _{m,G}\)

\(\Delta _{s,G}\)

\(\Delta _{m,R}\)

\(\Delta _{s,R}\)

\(\mathrm{NYC}_{\mathrm{bike}}\)

0.06

0.08

0.46

0.15

0.72

0.12

86%

49%

91%

37%

\(\mathrm{NYC}_{\mathrm{taxi}}\)

0.09

0.11

0.53

0.14

0.83

0.15

83%

22%

89%

29%

\(\mathrm{CHI}_{\mathrm{bike}}\)

0.14

0.16

0.29

0.25

0.56

0.26

51%

35%

75%

38%

\(\mathrm{CHI}_{\mathrm{taxi}}\)

0.08

0.09

0.39

0.11

0.79

0.13

80%

21%

90%

30%