A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Table 2

Initial variables set of traffic incident detection.

Variable serial number	Initial variable
1	Measured flow rate of upstream detector
2	Measured speed of upstream detector
3	Measured occupancy of upstream detector
4	Measured flow rate of downstream detector
5	Measured speed of downstream detector
6	Measured occupancy of downstream detectors
7	Ratio of occupancy to flow measured at the same time by the same detector
8	Ratio of occupancy to velocity measured at the same time by the same detector
9	Ratio of flow to velocity measured at the same time by the same detector
10	Ratio of measured flow and predicted flow of upstream detector
11	Ratio of measured speed to predicted speed of upstream detector
12	Ratio of measured occupancy rate to predicted occupancy rate of upstream detector
13	Ratio of measured flow to predicted flow of downstream detector
14	Ratio of measured speed to predicted speed of downstream detector
15	Ratio of measured occupancy rate to predicted occupancy rate of downstream detector
16	Flow ratio of adjacent upstream and downstream detectors at the same time
17	Speed ratio of adjacent upstream and downstream detectors at the same time
18	Acquisition occupancy ratio of adjacent upstream and downstream detectors at the same time