Contextual layers in ATIP could emphasize nearby POIs -- shops, public spaces, big transport hubs, greenspace, etc. We should think through how to visualize this, and how much we could auto-score new routes drawn based on what types of amenities it passes by. We can also think about this in context of improving access to amenities.
See https://dabreegster.github.io/talks/tds_seminar_synthpop/slides.html#/trip-attractor-tables-for-destinations for very vague notes about using this kind of data with a travel demand modeling. Instead of equally weighting a corner store and a big supermarket for the activity of grocery shopping, we could maybe use some kind of ML (if we have data on what types of shops people actually visit?) to tune weights more specifically.
The silly codename previously used for this: DestAny
robinlovelace-ate
commented
1 year ago
Contextual layers in ATIP could emphasize nearby POIs -- shops, public spaces, big transport hubs, greenspace, etc. We should think through how to visualize this, and how much we could auto-score new routes drawn based on what types of amenities it passes by. We can also think about this in context of improving access to amenities.
See https://dabreegster.github.io/talks/tds_seminar_synthpop/slides.html#/trip-attractor-tables-for-destinations for very vague notes about using this kind of data with a travel demand modeling. Instead of equally weighting a corner store and a big supermarket for the activity of grocery shopping, we could maybe use some kind of ML (if we have data on what types of shops people actually visit?) to tune weights more specifically.
The silly codename previously used for this: DestAny