Kim and Hirtle (1995) suggested that knowledge of routes is represented as a sequence of intersection-based choice points where procedural decisions must be made, and Lupien et al. elaborate to point out that route complexity is contingent upon the number of intersection-based decision points (Lupien et al., 1998).
It creates routes from source to destination based on sequence of connected intersections.
GyTAR creates routes from source to destination based on sequence of connected intersections.
We use a sequence ([p_1, p_2, ldots, p_n]), which consists of a source, a destination, and a sequence of consecutive road intersections in-between, to represent a route.
Consequently, in order to address the influences from the above issues, a well-designed routing protocol often consists of two steps: (1) select an optimal route, consisting of a sequence of passed road intersections; (2) select the next hop, usually through greedy forwarding.
Repeat the process indefinitely, because (lim_{krightarrow+infty }y_{A_{k}^=y_{B^), the intersection sequence of orbit which passes through R and the phase set N will be attracted to point (B^) eventually.
Similarly, if (y_{R}in [y_{B_{i}^,y_{B_{i+1}^)_{i=0,1,2,ldots}), we also can obtain the intersection sequence of orbit which passes through R and the phase set N will be attracted to point (B^) eventually. Therefore, the orbit starting from arbitrary point below (A_{0}) eventually is attracted to the periodic solution (widehat{B^BB^).
In order to test this observation further we performed the same enrichment analysis separately for the three groups of sequences: i) intersection of the two datasets, ii) the binding events present only in the CM dataset and iii) the binding events present only in the E2T dataset.
