ladot_analysis_dataprep | LADOT Analysis Tool Data Prep | Data Visualization library

Install Python for your OS (Anaconda strongly recommended). Install osmosis for your OS. Clone/download this repository and navigate to it from a command line terminal.
Install Python for your OS (Anaconda strongly recommended).
Install osmosis for your OS.
Clone/download this repository and navigate to it from a command line terminal.
Install dependencies: conda env create -f environment.yml
Activate conda environment: conda activate gencosts
†Note: Generalized cost generation can be executed without the use of local data by running the script with the -i (no infrustructure data) or -v (no volume/speed data) flags. If you do want to use local data but your filenames are different from those specified at the top of the script, you can edit them manually there.
Copy local data files† into the data directory, including: stop signs traffic signalization bikeways crosswalks traffic volume and speed data
If working with a static, local OSM extract, put your your .osm file into the data directory as well.
To run the analysis with all defaults, simply navigate to the root directory of this repository and execute the following command: python osm_gen_costs.py
Or specify a place name to test things out on a smaller geographic area: python osm_gen_costs.py -p "Financial District, Los Angeles"
To use a local .osm OSM XML file instead of pulling OSM data from on-the-fly, you can use the -o flag: python osm_gen_costs.py -o <your_osm_file.osm>
Or if you've run this script before, you can save time by using the -d flag and pointing the script to the elevation data (DEM) .tif that was generated on-the-fly last time the script was run: python osm_gen_costs.py -d <your_dem_file.tif>
If you would rather store your output data ESRI shapefiles instead of .pbf, simply use the -s flag and the script will generate two sets of shapefiles for the node and edge data. python osm_gen_costs.py -s shp
The script will then generate an OSM XML file with the computed attributes stored as new OSM way tags. The following new tags are created by default: speed_peak:forward -- speed during hours of peak traffic in the forward direction speed_peak:backward -- speed during hours of peak traffic in the reverse direction speed_offpeak:forward -- speed during offpeak traffic hours in the forward direction speed_offpeak:backward -- speed during offpeak traffic hours in the reverse direction slope_1:forward -- % distance with 2-4% slope in the forward direction slope_2:forward -- % distance with 4-6% slope in the forward direction slope_3:forward -- % distance with 6+% slope in the forward direction slope_4:forward -- % distance with 10+% slope in the forward direction slope_1:backward -- % distance with 2-4% slope in the reverse direction slope_2:backward -- % distance with 4-6% slope in the reverse direction slope_3:backward -- % distance with 6+% slope in the reverse direction slope_4:backward -- % distance with 10+% slope in the reverse direction self_aadt -- annual average daily traffic on the edge cross_aadt:forward -- annual average daily cross-traffic on the edge in the forward direction cross_aadt:backward -- annual average daily cross-traffic on the edge in the reverse direction parallel_aadt:forward -- annual average daily parallel-traffic on the edge in the forward direction parallel_aadt:backward -- annual average daily parallel-traffic on the edge in the reverse direction control_type:forward -- stop sign or traffic signal in the forward direction control_type:backward -- stop sign or traffic signal in the reverse direction bike_infra:forward -- bike paths, lanes or boulevards in the forward direction bike_infra:backward -- bike paths, lanes or boulevards in the reverse direction unpaved_alley -- edge is an unpaved alley busy -- edge is tertiary road type or above xwalk:forward -- crosswalk in the forward direction xwalk:backward -- crosswalk in the reverse direction