Research Article
In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning
Table 1
Comparison of images, minimum mean square error, and cosine similarity of three prediction results.
| The name of the train station in the regional center, the name of the line to which it belongs, and the opening time | Hefei railway station in Yuliu railway (2013/12) | Huishan railway station in Shanghai-Nanjing railway (2010/07) | Xinggongtang station in Xuanhang railway (1994/07) |
| Data distribution before the completion of the railway | | | | The true distribution of new data within three years after the completion of the railway | | | | MMSE loss | Image | | | | MMSE value cosine similarity | 0.000320737297485258 | 0.000017449833468964 | 0.000006898057400988 | 0.130015477494133562 | 0.077197126502542367 | 0.099417792856082382 | Cosine loss | Image | | | | MMSE value cosine similarity | 1.5686035808463898 | 0.3253649305977068 | 0.056709321859160375 | 0.586250486851695762 | 0.31263808319331287 | 0.697352200764660382 | 2D-DFT loss | Image | | | | MMSE value cosine similarity | 0.29219522888094723 | 0.059963647176112536 | 0.02021538911975866 | 0.148902076050230262 | 0.098727064405085867 | 0.079767883030792582 | MMSE-2D-DFT loss | Image | | | | MMSE value cosine similarity | 0.000303084697194027 | 0.000009756472944948 | 0.000003572309352900 | 0.263564482924703362 | 0.111119785306437367 | 0.134587089183047082 | Cosine-2D-DFT loss | Image | | | | MMSE value cosine similarity | 4.620754636255468 | 0.7541079039032181 | 0.14423271414878933 | 0.597224169228503626 | 0.33967178745360267 | 0.662771618719908282 | MMSE- cosine loss | Image | | | | MMSE value cosine similarity | 0.000364660292423330 | 0.000165248184589094 | 0.000053113823069145 | 0.577345932340722066 | 0.338455365847816467 | 0.556173118171807028 | MMSE- Cosine-2D-DFT loss | Image | | | | MMSE value cosine similarity | 0.000366539845216779 | 0.000247506640524558 | 0.000071690391093354 | 0.564076775017910262 | 0.341916216360121467 | 0.606259892930003782 |
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