Table 10.

Time series forecasting results under decelerated conditions.

Dataset	Metric	CNN [15]	Deep RNNs [38]	LSTM [17]	Transformer [31]	Informer
I-D	MAE	$2.1981\times {10}^{-2}$	$1.8724\times {10}^{-2}$	$2.1514\times {10}^{-2}$	$2.2651\times {10}^{-2}$	$1.4192\times {10}^{-2}$
	MSE	$1.1893\times {10}^{-3}$	$9.2163\times {10}^{-4}$	$1.3191\times {10}^{-3}$	$1.0206\times {10}^{-3}$	$5.2582\times {10}^{-4}$
	RMSE	$3.4487\times {10}^{-2}$	$3.0358\times {10}^{-2}$	$3.6320\times {10}^{-2}$	$3.1947\times {10}^{-2}$	$2.2931\times {10}^{-2}$
	Running Time (s)	480	2700	400	4295	169
O-D	MAE	$3.6730\times {10}^{-3}$	$3.9415\times {10}^{-3}$	$3.6925\times {10}^{-3}$	$3.9617\times {10}^{-3}$	$2.8098\times {10}^{-3}$
	MSE	$2.7954\times {10}^{-5}$	$3.5443\times {10}^{-5}$	$2.5712\times {10}^{-5}$	$3.3534\times {10}^{-5}$	$2.0841\times {10}^{-5}$
	RMSE	$5.2872\times {10}^{-3}$	$5.9534\times {10}^{-3}$	$5.0708\times {10}^{-3}$	$5.7908\times {10}^{-3}$	$4.5652\times {10}^{-3}$
	Running Time (s)	480	2550	400	4344	171
R-D	MAE	$4.2048\times {10}^{-3}$	$5.8855\times {10}^{-3}$	$6.1152\times {10}^{-3}$	$4.1870\times {10}^{-3}$	$5.4478\times {10}^{-3}$
	MSE	$2.9625\times {10}^{-5}$	$5.9285\times {10}^{-5}$	$6.0482\times {10}^{-5}$	$3.0447\times {10}^{-5}$	$4.9924\times {10}^{-5}$
	RMSE	$5.4429\times {10}^{-3}$	$7.6997\times {10}^{-3}$	$7.7770\times {10}^{-3}$	$5.5179\times {10}^{-3}$	$7.0657\times {10}^{-3}$
	Running Time (s)	480	2475	400	4451	182