Research Article
Traffic Management as a Service: The Traffic Flow Pattern Classification Problem
Table 1
Number of clusters obtained and associated statistics.
| | | Mo | Tu | We | Th | Fr | Sa | Su |
| | A: Week 1 | 3 | 3 | 1 | 2 | 1 | 2 | 3 | | B: Week 2 | 1 | 2 | 4 | 1 | 3 | 2 | 4 | | C: Week 3 | 5 | 3 | 1 | 4 | 2 | 2 | 3 | | D: Week 4 | 3 | 1 | 1 | 3 | 3 | 2 | 1 | | E: mean(A, B, C, D) | 3 | 2.25 | 1.75 | 2.5 | 2.25 | 2 | 2.75 | | F: median(A, B, C, D) | 3 | 2.5 | 1 | 2.5 | 2.5 | 2 | 3 |
| | G: average day | 4 | 2 | 2 | 4 | 3 | 2 | 2 | | H: round(E) == G | False | True | True | False | False | True | False | | I: mean(E, F, G) | 3.3() | — | — | 3 | 2.58() | — | 2.58() |
| | Number of clusters | 3 | 2 | 2 | 3 | 3 | 2 | 3 |
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