An Update on Region Radio — Using AI to Disseminate Information on Environmental and Cultural Conservation through Story Telling

This is a guest post by Cassidy McDonnell. See Cassidy’s bio and the bottom of this post.

Have you ever driven by an interesting building or an intriguing trailhead? You might glance up from the road, think “hmm, I wonder what that could be”, ponder for a moment or two, and then sleepily continue on your drive. You make a mental note to look up the landmark later when all of a sudden, a small voice from the backseat squeals about a “bathroom emergency” and before you know it, you’re scrambling towards the closest rest stop praying that you make it there before it’s too late.

Your mental note about the landmark flies out the window with the miles that pass and by the time you’re breathing a sigh of relief and patting yourself on the back for yet another averted crisis, all previous thoughts about that intriguing sculpture you passed pre-”bathroom emergency” have vanished. By the time you pack everyone into the car and get back on the road, all you can hear is the static on the radio and the GPS announcing that you need to turn right in 227 miles.

What if, instead of enduring those annoying advertisements and songs you’ve heard a thousand times before, you could learn all about these mysterious places right on the spot? That’s where Region Radio comes in. Although resembling a podcast (or any other storytelling device) in form and function, Region Radio has quite a lot going on behind the scenes. This program is a place-specific learning tool that facilitates interactions between users and the environments in which they find themselves. Focused on environmental and historical preservation, Region Radio aims to share the untold and under-told stories of places that are often glazed over or entirely forgotten in mainstream narrative settings. This is consistent with Vanderbilt’s outreach role within CompSustNet.

How does it work?

Region Radio creates a playlist for each trip using a recursive backtracking algorithm. It first looks at the entire trip and focuses in on the final destination. It then searches the web for an interesting story about that ending point. Once it finds a story that is interesting to the user (we’ll call it “Story 1”), it adds that story to its playlist and reanalyzes the trip from the starting point and ending at the opening of Story 1. 

Figure 1: Example Playlist Construction: The playlist is built backwards starting by finding an interesting story about point B. The time it takes to tell Story 1 determines the location of point B1, which determines the subject of Story 2. Each story is determined and built from the stories that follow it in the playlist.

Figures 1 and 2 display a visual example of how the program works. For the 30-minute trip shown, the program searches the radius around point B (depicted in Figure 2) . In this example, a six-minute story is found, added to the very end of the playlist, and labeled “Story 1”. Region Radio then treats point B1 as the end of the trip and searches for an interesting story within its designated radius. Within this radius, the program finds Story 2, and Story 2 is added to the queue immediately before Story 1, playing as the vehicle approaches point B1. This strategy is repeated for the entire trip, eventually compiling a full playlist from point A to point B.

See the caption
Figure 2: Example radii: for each point, Region Radio searches within a given radius to find a landmark with an interesting story. Radii distances can be adjusted based on story density and interestingness for each location.

Region Radio is also programed to adjust the size of the search radius depending on the density of interesting stories found in a given area. So, for example, if the program doesn’t find any interesting stories in the initial search radius, it will expand the radius until it comes back with a story that RR believes the user will want to hear. If many stories are collected within the initial radius, the story delivered to the user is determined by length (longer stories are prioritized), user preferences and interests, and whether or not a user has heard a particular story before.

Our team is currently working on improving the user’s experience by developing parameters for determining story interestingness and place relevance as well as improving the program’s audio aesthetics. Currently, we are using a Google text-to-speech API to translate written stories to audio files. Although this API uses more inflection, emotion, and vocal variation than the Amazon Polly API (which the program was using previously), the Google version is still rather robotic in nature, which can make stories sound less interesting. In order to combat this hurdle, our group is currently working to include existing podcasts in the story playlists and recruit student narrators to read stories aloud for the program. We expect that these developments will improve user experiences by making stories more engaging.

Additionally, we are currently developing parameters to determine “interestingness” and “place relevance” of suggested stories as well as create connections between those stories. These parameters will eventually be implemented into the program in order to create playlists that are even more relevant and captivating.

Check out earlier posts on the CompSustNet blog on Region Radio and Related Works and on Heightening Environmental Responsibility through Place Attachment, as well as a recent paper presented at the International Conference on Computational Creativity (p. 336 of the proceedings) entitled Region Radio: An AI that Finds and Tells Stories about Places by Douglas H. Fisher, and last year’s undergraduate research assistants: Emily Markert, Abigail Roberts and Kamala Varma .

Region Radio began as a collaboration between the CompSustNet  lab at Vanderbilt University and the Space, Learning, and Mobility lab at Vanderbilt University.  Research and development of Region Radio has been supported by NSF Award #1521672 “Collaborative Research: CompSustNet: Expanding the Horizons of Computational Sustainability” and NSF Award #1623690 “EXP: Bridging Learning in Urban Extended Spaces (BLUES) 2.0”

Cassidy McDonnell is a May 2019 graduate of Vanderbilt University in Civil Engineering. She became interested in Computational Sustainability through Vanderbilt’s University Course on the Ethics of Artificial Intelligence, which included a module on Computational Sustainability. Cassidy can be reached at cassidy.a.mcdonnell@vanderbilt.edu. The opinions expressed herein are Cassidy’s and do not necessarily represent the opinions of Cornell University.