With the above factor structure, the resulting cost surface is in a
raster format with individual pixels indicating suitability scores,
ranging from 1 to 5. Using the cost surfaces, the shortest path
analysis identifies the pixels with the least possible scores that can
connect the two points, and this is the reason why a reversed scale
is used. For research convenience, the departure and destination
points were set to the major airports in Austin and San Antonio, TX.
Station location involves a different set of decision-making procedures
and requires a separate in-depth study. For this reason,
making decisions as to the location of stations is not part of this
study. The author used the cities’ two main airports as international
airports increasingly linked to HSR route in metropolitan areas
worldwide.
Fig. 1(a)e(f) show the optimal HSR route for each factor. Routes
1 and 5 produced unique trajectory, while the remaining paths
resulting from Factors 2 to 4 look relatively similar in their shapes.
Route 1 minimized passing through major population centers.
Other than areas immediately surrounding the starting and ending
points, most highly populated areas remain intact. Route 2 avoids
major population centers to reduce noise effect and minimizes
crossing highways. Routes 3 and 4 are similarly shaped because the
major water and ground resources, their variables intend to avoid,
are concentrated largely on the east side of the study area. Route 5
shows a distinct result as it attempts to detour widely toward the
northwest part of the study area.