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BTP
3.0
Routing/ClimbAnalysis/PowerCalculation
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class to perform routing routines on osm data sets More...
#include <Routing.h>
Public Member Functions | |
| Routing (OSM *o) | |
| short | DijkstraFib (KOO *s, KOO *e, char max, char min, OSM *o, int Si) |
| this is famous Dijkstra shortes path algothim More... | |
| short | DijkstraFib_minHM (KOO *s, KOO *e, char max, char min, OSM *o, int Si) |
| see DijkstraFib(), but minimizing Hm instead of distance | |
| void | extract_neighbourlist (STRONG *s, STRONG *e, Neighbour ***nl, int *nc) |
| collects a Neighbour list from STRONG network More... | |
| void | resetSTRONG (KOO *k, int STRONGindex) |
| resets the STRONG network to begin calculation | |
| void | finish_STRONG () |
| resets STRONG network of a STRONGcalc to normal Dijkstra state More... | |
| STRONGlayer * | STRONGlayer_create (STRONGlayer *SL, int N) |
| creates STRONGlayer from a unfinalized STRONG network | |
| STRONGlayer * | STRONGlayer_create (int N) |
| creates STRONGlayer from a unfinalized STRONG network | |
| STRONGlayer * | STRONGlayer_load (char *filename) |
| loades STRONGlayer from file | |
| void | STRONGlayer_save (char *filename, STRONGlayer *SL) |
| saves STRONGlayer to file | |
Private Member Functions | |
| void | setSTRONG (KOO *k, KOO *s, KOO *e, float radius, int n) |
| body of finish_STRONG(); | |
| void | STRONGlayer_reset_d (KOO *k) |
| sets Neighbour distance attribute d to zero | |
| void | STRONGlayer_reconstruct_d (KOO *k) |
| reconstructs Neighbour distance attribute d from Way data | |
| void | STRONGlayer_count_neighbours (KOO *k, int *nbc) |
| counts total Neighbour count on osm data set | |
| void | STRONGlayer_mark_neighbours (KOO *k, int si) |
| increment Neighbour attribute d by its usage rate on STRONG network | |
| void | STRONGlayer_fill_neighbours (KOO *k, STRONGlayerele *data, int *nbc) |
| copy Neighbour attribut d to STRONGlayer data | |
| void | STRONGlayer_sort (STRONGlayer *SL, long s, long e) |
| quicksort neighbours by its usage rate | |
Private Attributes | |
| OSM * | o |
| char ** | SLgrid |
| int | nlat |
| int | nlon |
| double | SLlonmax |
| double | SLlatmax |
| double | SLlonmin |
| double | SLlatmin |
| double | dlat |
| double | dlon |
| void | STRONGlayer_grid_new () |
| void | STRONGlayer_grid_reset () |
| short | STRONGlayer_grid_free (KOO *k) |
| void | STRONGlayer_grid_delete () |
class to perform routing routines on osm data sets
this class provides routing routines to work on an Neighbour network. STRONGcalc does this as well, but in a seperate thread. Here, Dijkstra algorithms are implemented. Additionally this class handels the STRONGlayer buisness, i.g. creating map layers from finished STRONGcalc calculation.
this is famous Dijkstra shortes path algothim
calculates shortes path on an osm dataset using Dijkstra algorithm. a FibunacciHeap priority queue is used for best performance. When algorithm finishes there will we a network of STRONG references from which the shortes paths to the startpoint s can be traced back by iterating over the KOO->cd->s[Si]->from reference. The algorithm respects the road restrictions min and max describing allowed road types. If no way to the end point e is found zero will be returned, otherwise 1.
Definition at line 8 of file Routing.cpp.
| void Routing::finish_STRONG | ( | ) |
resets STRONG network of a STRONGcalc to normal Dijkstra state
normal state of STRONG network consistsof crossdata with STRONG arrays of length two. Dijkstra will usually use only one of them but when modifying a track both ar used. In the State for STRONGcalc these arrays may contain several 100 elements depending on the setup of the STRONGcalc
Definition at line 197 of file Routing.cpp.
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private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
Definition at line 307 of file Routing.cpp.
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private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
Definition at line 297 of file Routing.cpp.
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private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
Definition at line 277 of file Routing.cpp.
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private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
Definition at line 292 of file Routing.cpp.
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
|
private |
grid to decide whether a cross can still be used to mark neighbours
to homogeniously probe the STRONG network to reconstruct the STRONGlayer it is necessary to check that probe points, i.g. cross are equally distant to each other. Therefor a net with 0.5x0.5 km cells is created, first cross belonging to this cell will probe the STRONG network, all following cross belonging to this cell will be ignored thus avoiding overjudging of regions with high cross density, e.g. big citys
1.8.5
