OD-cost matrix - Choose best destination within a spesific distance based on attribute in destinations layer

EdvardFåkvam

Hi guys! Im fairly new to this forum, so forgive me if i am asking in the wrong place... I am currently working on a network analysis where i have used the OD-cost matrix network analysis tool to find the distance (length and travel time) to the nearest busstop for a population. My data is organized in points representing a 100x100 meter area inahbitted by atleast one person. Currently i only have the distance to the closest busstop, and it doesn't take into account how many departures there are from each stop. I have the departure frequency data in the attribute table of the destinations layer (busstops as points). What i would like to do is to rather than find the closest stop for each population point, is to find out what is the busstop with the highest frequensy of departures within a given travel time / distance along the network. etc 300,600, 1000 meters. Are there any of you who have any experience with the OD-cost matrix tool, that knows how i could achieve this?

Melinda Morang

Hello Edvard.

This should be doable.  I think the best way is to find all bus stops within the maximum travel time/distance limit of each origin and post-process the results.

Configure the OD Cost Matrix so that it doesn't have a limit on the number of destinations to find, but use a limit for the travel time/distance.  (You could do three separate analyses for 300, 600, and 1000, or you could do it all in one using the maximum limit.)  This results in multiple rows in the Lines output for each origin point.

Do some joins to transfer the frequency of service at each bus stop to the Lines output.  Exactly how you do this depends on whether you're using Pro and an NA layer or Python or ArcGIS Online.  Your question doesn't say. 🙂  I can help further if necessary and if you share some more information.

Post-process the resulting table to identify the highest frequency stop within the desired distance limit of each origin.  This should be relatively easy using any type of database operation (SQL, Pandas, etc.).  You could probably do this with various Geoprocessing tools, too, like Sort and Delete Identical, maybe...?