In order to determine the most efficient path for the new Skytrain line, we first had to determine where our priorities would lie. this was necessary in order to make a Cost Surface across which a Least-Cost path could be generated. A cost table, while being a necessary component, is a good place to begin thinking about the many sectors that would feel the impacts, whether negative or positive, of this new proposed Transit line.
Below is the table that we collectively decided on. It is worth noting that these cost values are subjective, but assigned with careful consideration.
Due to the nature of light rails such as Skytrains, they can transcend many geographical boundaries within a city, as they have the freedom to be suspended above or buried beneath city streets. This is why no land use type is given a friction value greater than 60, so that while the algorithms are discouraged from crossing through these areas, they can if it would save costs. Residential areas are given the highest friction values because we wanted this Skytrain to be the least intrusive as it could be. Avoiding residential areas would reduce noise pollution in areas where it is particularly undesirable. Apartments, arterial roads and existing railways were given the lowest values because the Expo, Millennium, and Evergreen lines already travel through these types of land and so these designations would align best with Translink’s previously held priorities. Building along these land types also help to target areas of dense populations.
Taking these informed, yet subjective friction values, this cost surface raster was generated through the use of the Conversion Tool, “To Raster” with the value field set as the assigned costs:
Using this cost surface, we performed 4 Cost Distance analyses using the ArcToolbox > Spatial Analyst Tools > Distance > Cost Distance tool. Each analysis used 1 of the 4 stations. The first one, for example, used Broadway Station as the input feature source data for the Cost Distance Tool. This process generated the following 4 routes that would be “cheapest” to implement.
It is important to note that the costs that are being represented and analyzed in ArcMap are not explicitly financial. They are the costs that we subjectively assumed in the table above. These relative costs, however, are closely linked to financial end costs.
Two of the generated paths followed the same path from station to UBC as the one generated from an adjacent starting point, so we determined that Broadway Station and Oakridge Station provided the two best options for cost deliberation. The Broadway Station route followed Broadway until it hit University Boulevard, one of the main arteries out of UBC. The Oakridge Station route followed 41st Avenue until it hit Southwest Marine Drive, another main artery.
The next step was to quantify how many people would be serviced by having either one of these routes constructed nearby. We used Select by Location to select the Dissemination Areas closest to each route and used the population attribute to determine how many people would be directly impacted by increased accessibility to rapid transit near their homes. Below is an example of this process for the Broadway Station route.
