Table 4.

Error of fit between GPS data and synthetic data

GPS	$\mathit{\Delta }r$	$r_g$	$S^{\mathit{unc}}$	T	D	$\mathit{\Delta }t$	V	N	f(L)
MD
$\mathit{d}$-EPR	.0254	.0148	1.9855	.0053	.1334	.0738	.0123	.0113	.0323
	.5346	.2850	156.92	.0156	.2992	.7567	.1415	.0411	.2429
SWIM	.0229	–	3.8403	.0054	.1232	.0589	.0123	.0319	.0358
	.8970		210.87	.0156	.2634	.7321	.1522	1.6923	.4914
LATP	.0258	.0225	3.7636	.0054	.1233	.0655	.0178	.0315	.0324
	.5968	.9508	151.35	.0157	.2636	.7148	.4639	1.9085	.3811
RD
d-EPR	.0031	.0237	–	.0231	.0923	.0349	.0042	.0271	.0560
	.0420	.9939		.1906	1.2493	.4221	.0360	3.3216	.5258
SWIM	.0274	–	–	.0231	–	.2647	.0102	–	.0915
	1.6628			.1912		1.4443	.0919		3.6641
LATP	.0169	–	–	.0231	–	.1599	.0168	–	.0899
	.1381			.1912		1.1524	.3609		2.9663
WT
d-EPR	.0069	.0223	–	.0231	.0923	.0291	.0045	.0270	.0530
	.0518	.8217		.1906	1.0593	.4369	.0394	2.132	.4623
SWIM	.0180	–	–	.0231	.0923	.1608	.0095	–	.0908
	.7278			.1912	.9510	1.0941	.0823		3.2346
LATP	.0190	–	–	.0231	.0923	.1027	.0166	–	.0890
	.1840			.1913	1.0398	.9187	.4282		2.6838
Best model	d-EPR	d-EPR	d-EPR	d-EPR	SWIM	d-EPR	SWIM	d-EPR	d-EPR
	RD	MD	MD	MD	WT	WT	WT	MD	MD

Every row i is a model and every column j a mobility measure. A cell (i, j) indicates the RMSE (first row) and the KL divergence (second row) of a synthetic distribution w.r.t. the real distribution. The best RMSE values are in italic. Symbol—indicates that the synthetic distribution is not comparable with the real distribution. We highlight in bold the combination of temporal and spatial model leading to the highest number of italic cells