Before any optimizations of the UDP's operations are done,
a **demand forecasting** system for *tactical* purposes is implemented.
To achieve that, the cities first undergo a **gridification** step
where each *pickup* location is assigned into a pixel on a "checker board"-like grid.
The main part of the source code that implements that is in this [file](https://github.com/webartifex/urban-meal-delivery/blob/main/src/urban_meal_delivery/db/grids.py#L60).
Visualizations of the various grids can be found in the [visualizations/](https://github.com/webartifex/urban-meal-delivery/tree/main/research/visualizations) folder
and in this [notebook](https://nbviewer.jupyter.org/github/webartifex/urban-meal-delivery/blob/main/research/03_grid_visualizations.ipynb).
Then, demand is aggregated on a per-pixel level
and different kinds of order time series are generated.
The latter are the input to different kinds of forecasting `*Model`s.
They all have in common that they predict demand into the *short-term* future (e.g., one hour)
and are thus used for tactical purposes, in particular predictive routing (cf., next section).
The details of how this works can be found in the first academic paper
published in the context of this research project
and titled "*Real-time Demand Forecasting for an Urban Delivery Platform*"
(cf., the [repository](https://github.com/webartifex/urban-meal-delivery-demand-forecasting) with the LaTeX files).
All demand forecasting related code is in the [forecasts/](https://github.com/webartifex/urban-meal-delivery/tree/main/src/urban_meal_delivery/forecasts) sub-package.
