Name
Abstract | ||
|---|---|---|
Route planning has always provided a rich vein of research for the artificial intelligence community. However, to date the
majority of this research has focused on the generation of optimal routes using shortest path algorithms to minimise distance
traveled. The problem of planning high quality realistic routes is difficult for several reasons. Firstly, real maps rarely
contain the sort of information that is useful for constructing realistic, high quality routes (real-time traffic information
or road quality data). Secondly, the notion of “route quality” is notoriously difficult to define and is likely to change
from person to person. Consequently, in real-world route planning situations, the shortest route is rarely the best route
for a given user.
|
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-44533-1_80 | PRICAI |
Keywords | Field | DocType |
personalised route planning system,artificial intelligent,shortest path algorithm | Route planning,Shortest path problem,Computer science,sort,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-67925-1 | 8 | 3.63 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lorraine Mcginty | 1 | 687 | 48.17 |
| Barry Smyth | 2 | 5711 | 414.55 |