A set of Jupyter notebooks to support Road network analytics based on Open-Source and Open-Data Geospatial Planning & Budgeting Platform (GPBP) Transport sector use cases
FULL DOCUMENTATION IS AVAILABLE, but a quick preview of the resources developed is provided below:
The notebooks are divided in four separate groups, from building the analytics models from a variety of data sources to computing estimates of the impact of changes to the transportation network.
Performs all the data import
Quick summary statistics on the road model can be seen explored: VISUALIZE IT!
Quick summary statistics on the population data loaded can also be seen explored: VISUALIZE IT!
In a nutshell: Imports the OSM network into a computationally-efficient format
We can see the imported result on a browser VISUALIZE IT! (it may take time to open)
In a nutshell: Imports population data from Raster format into a computationally-efficient and aggregated into customizable polygons
In a nutshell: Imports population data from Raster format into a vector format
A heatmap shows the distribution of the population VISUALIZE IT!
In a nutshell: Aggregates population data into analysis zones with geographic resolution proportional to population density
[VISUALIZE IT!](https://nbviewer.org/github/pedrocamargo/road_analytics/blob/main/notebooks/1.2.2_Population into hex and clustering.ipynb)
In a nutshell: Imports several classes of Point-of-Interest data from OSM into the model's database
This jupyter notebook includes the visualization of all hospitals, schools and airports imported and can visualized -> UNDER DEVELOPMENT
Begins the modelling process per se by incorporating a series of assumptions aligned with best-practices from the transport modelling world to turn the analytics model into a simplified transport model capable of providing traffic estimates for any link in the road network model.
In a nutshell: Assign speeds and road capacities as a function of road type and pavement type/condition.
This jupyter notebook includes a map showing the routes in the network with the highest capacity and can visualized. VISUALIZE IT!
In a nutshell: Produces transportation demand matrices based on the population and PoI imported
This jupyter notebook includes a map showing the traffic distribution in an abstract map that effectively shows the overall traffic demand across the network and can visualized -> UNDER DEVELOPMENT.
In a nutshell: Incorporates mobility data to calibrate and/or replace the simplified demand model developed on 2.2
In a nutshell: Processes the CVTS data for Vietnam to obtain trip matrices that can be used in conjunction with the personal travel demand matrices produced on 2.2.
In a nutshell: Generalizable use-cases that may be of interest in multiple countries
In a nutshell: Computes the an estimate of the traffic for any given link. It also allows for extracting the origins and destinations using said link/asset
NOTEBOOK UNDER DEVELOPMENT.
In a nutshell: Identifies the sections (links/assets) in the network that are most likely to be bottlenecks as a function of their capacity and estimated traffic volumes.
NOTEBOOK UNDER DEVELOPMENT.
In a nutshell: Computes the impact of the disruption of each one of the 10% of links with the highest demand (can use either synthetic data or from mobility data)
NOTEBOOK UNDER DEVELOPMENT.
In a nutshell: Identifies the population cut-off from hospital access for a given flooding scenario
NOTEBOOK UNDER DEVELOPMENT.