You know the drill. A list of addresses lands in your inbox and you need a quick read on traffic before you can argue about rents. You open a map, hunt for DOT stations, copy AADT into a sheet, and hope you did not miss a nearby counter that changes the story. It is repetitive work that still matters. Traffic volume feeds your location call and affects your rent view, but the way most teams pull it together is slow and easy to mess up. Research ~8 min to run Map Traffic Counts at Candidate Sites Vic prompt Use Vic to map traffic counts for candidate sites with their nearest DOT stations. Purpose Traffic volume data informs location decisions and rent projections. The task reduces analyst time from roughly 75 minutes to about 8 minutes per set of sites. Inputs Addresses Required Outputs An interactive map artifact showing sites and stations plus a sortable comparison table ready for export or further review. Time saved Turns roughly 75 minutes of manual work into about 8 minutes. How it works Give Vic a list of addresses, up to 25 sites. That is the only required input. Vic places each site on an interactive map and finds nearby DOT traffic count stations. The map uses color matched markers so you can see which stations tie to which site at a glance. Run it with: Use Vic to map traffic counts for candidate sites with their nearest DOT stations. Alongside the map, Vic builds a sortable comparison table. Each row ties a site to a nearby station and includes the primary road, road class, AADT, count year, station distance, and other nearby roads. It also flags incomplete data so you know where gaps exist before you rely on a number. The output is built for quick decisions and clean handoffs. You can scan the map to spot outliers, then sort the table by AADT or distance to rank candidates. The fields are standardized, so the table is ready for export or review without cleanup. A small detail that helps is the pairing of sites to stations on the map. When you compare multiple corridors, visual clutter slows you down. Color matching keeps each site’s context tight, so you do not waste time guessing which counter belongs to which address. The table is where the work usually drags when done by hand. Pulling AADT, checking the count year, and keeping distances consistent takes longer than it should. Here it is assembled in one pass, with clear labels and flags where data is thin. You still make the call, but you are not chasing inputs. This is for acquisition analysts, site selectors, and brokers who need a first read that holds up in a meeting. It works for retail corridors, industrial access points, and multifamily sites where traffic context matters. You can run it on a tight set or a broader screen and get the same structure back. The time difference is the point. What used to take about 75 minutes of clicking and copying comes back in about 8 minutes. That is the difference between squeezing this into a busy day and putting it off. Traffic counts alone do not decide a deal. But a clean, comparable view of AADT and proximity across your candidates makes the conversation sharper. You spend your time on judgment instead of assembly.