Skip to content

Latest commit

 

History

History
237 lines (198 loc) · 17.7 KB

README.md

File metadata and controls

237 lines (198 loc) · 17.7 KB
Raw

LADOT Analysis Tool Data Prep

This repository houses Python scripts to build networks and land use data for accessibility applications.

Setting up your development environment

  1. Install Python for your OS (Anaconda strongly recommended).

  2. Install osmosis for your OS.

  3. Clone/download this repository and navigate to it from a command line terminal.

  4. Install dependencies:

    conda env create -f environment.yml

  5. Activate conda environment:

    conda activate gencosts

Network

The osm_gen_costs.py script is designed to generate OSM-based, generalized cost-weighted networks for bicycle and pedestrian accessibility. The generalized cost formulas used here are an adaptation of Broach (2016).

How to Build the Network

  1. Copy local data files† into the data directory, including:
    • stop signs
    • traffic signalization
    • bikeways
    • crosswalks
    • traffic volume and speed data
  2. If working with a static, local OSM extract, put your your .osm file into the data directory as well.
  3. 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
  4. Or specify a place name to test things out on a smaller geographic area: 
    python osm_gen_costs.py -p "Financial District, Los Angeles"
  5. 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>
  6. 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>
  7. 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
  8. 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

†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.

Generalized Costs Calculations

Bicycle

Length Adjusted Metric Length Multiplier* Variable Name Notes
distance 1.0 distance
bike boulevard -0.108 bike_blvd_penalty OSM: cycleway="shared" OR LADOT: bikeway=("Route" OR "Shared Route")
bike path -0.16 bike_path_penalty OSM: highway="cycleway" OR (highway="path" & bicycle="dedicated") OR LADOT: bikeway="Path"
prop link slope 2-4% 0.371 slope_penalty upslope in forward direction, downslope in backward direction
prop link slope 4-6% 1.23 slope_penalty upslope in forward direction, downslope in backward direction
prop link slope 6%+ 3.239 slope_penalty upslope in forward direction, downslope in backward direction
no bike lane (10-20k) 0.368 no_bike_penalty OSM: cycleway=(NULL OR "no") OR OSM: bicycle="no" AND LADOT: bikeway=NULL
no bike lane (20-30k) 1.4 no_bike_penalty OSM: cycleway=(NULL OR "no") OR OSM: bicycle="no" AND LADOT: bikeway=NULL
no bike lane (30k+) 7.157 no_bike_penalty OSM: cycleway=(NULL OR "no") OR OSM: bicycle="no" AND LADOT: bikeway=NULL
Fixed Distance Metric Addt'l Distance (m)* Variable Name Notes
turns 54 turn_penalty assume additive ped turn penalty and scale other penalties based on the ratio of the coefficient to the original bike turns coefficient
stop signs 6 stop_penalty LADOT datasource: stop_yield
traffic signal 27 signal_penalty LADOT datasource: signalized_intersection
cross traffic (5-10k) 78 cross_traffic_penalty_ls left or straight only
cross traffic (10-20k) 81 cross_traffic_penalty_ls left or straight only
cross traffic (20k+) 424 cross_traffic_penalty_ls left or straight only
cross traffic (10k+) 50 cross_traffic_penalty_r right only
parallel traffic (10-20k) 117 parallel_traffic_penalty left only
parallel traffic (20k+) 297 parallel_traffic_penalty left only

*Multipliers and distances inspired by Broach (2016)

Generalized Cost Formula
gen_cost_bike:link distance + distance * (bike_blvd_penalty + bike_path_penalty + slope_penalty + no_bike_penalty)
gen_cost_bike:left turn_penalty + stop_penalty + signal_penalty + cross_traffic_penalty_ls + parallel_traffic_penalty
gen_cost_bike:straight stop_penalty + signal_penalty + cross_traffic_penalty_ls
gen_cost_bike:right turn_penalty + stop_penalty + signal_penalty + cross_traffic_penalty_r

Examples:

South Budlong Ave. Baxster Street
Way ID 165344383 161705335
From Node 123058787 5531221585
To Node 123058790 26187155
Length 89.023 225.923
gen_cost_bike:forward:link 89.023 818.9850357
gen_cost_bike:forward:left 81 60
gen_cost_bike:forward:straight 27 6
gen_cost_bike:forward:right 54 54
gen_cost_bike:backward:link 89.023 193.018
gen_cost_bike:backward:left 60 60
gen_cost_bike:backward:straight 6 6
gen_cost_bike:backward:right 54 54
slope_penalty:forward 0 3.243050056
slope_penalty:backward 0 0
bike_path_penalty:forward 0 0
bike_path_penalty:backward 0 0
bike_blvd_penalty:forward 0 0
bike_blvd_penalty:backward 0 0
signal_penalty:forward 0.021 0
signal_penalty:backward 0 0
stop_sign_penalty:forward 0 0.005
stop_sign_penalty:backward 0.005 0.005

Pedestrian

Length Adjusted Metric Length Multiplier* Variable Name Notes
distance 1.0 distance
prop link slope 10%+ 0.99 ped_slope_penalty upslope for forward direction, downslope for backward direction
unpaved or alleyway 0.51 unpaved_alley_penalty OSM: highway="alley" OR surface="unpaved"
busy street 0.14 busy_penalty OSM: highway=('tertiary' OR 'tertiary_link' OR 'secondary' OR 'secondary_link' OR 'primary' OR 'primary_link' OR 'trunk' OR 'trunk_link' OR 'motorway' OR 'motorway_link'
Fixed Distance Metric Addt'l Distance (m)* Variable Name Notes
turn 54 turn_penalty
unsignalized arterial crossing 73 unsig_art_xing_penalty left or right: ((13000 <= parallel traffic AADT <= 23000) OR (13000 <= self-edge AADT <= 23000)) AND (unsignalized) straight: (13000 <= cross traffic AADT <= 23000) AND (unsignalized)
collector crossing w/o crosswalk 28 unmarked_coll_xing_penalty left or right: ((10000 <= parallel traffic AADT < 13000) OR (10000 <= self-edge AADT < 13000)) AND (no crosswalk) straight: (10000 <= cross traffic AADT < 13000) AND (no crosswalk)

*Multipliers and distances inspired by Broach (2016)

Generalized Cost Formula
gen_cost_ped:link distance + distance * (slope_penalty + unpaved_alley_penalty + busy_penalty + nbd_penalty)
gen_cost_ped:left turn_penalty + unsig_art_xing_penalty + unmarked_coll_xing
gen_cost_ped:straight turn_penalty + unsig_art_xing_penalty + unmarked_coll_xing
gen_cost_ped:right turn_penalty + unsig_art_xing_penalty + unmarked_coll_xing

Examples:

Lanark Street
Way ID 13356087
From Node 123018756
To Node 368008589
gen_cost_ped:forward:link 54.416
gen_cost_ped:forward:left 54
gen_cost_ped:forward:straight 0
gen_cost_ped:forward:right 54
gen_cost_ped:backward:link 54.416
gen_cost_ped:backward:left 54
gen_cost_ped:backward:straight 73
gen_cost_ped:backward:right 54
unsig_art_xing_penalty_lr:forward 0
unsig_art_xing_penalty_s:forward 0
unsig_art_xing_penalty_lr:backward 0
unsig_art_xing_penalty_s:backward 73
unmarked_coll_xing_penalty_lr:forward 0
unmarked_coll_xing_penalty_s:forward 0
unmarked_coll_xing_penalty_lr:backward 0
unmarked_coll_xing_penalty_s:backward 0
ped_slope_penalty:forward 0
ped_slope_penalty:backward 0
unpaved_alley_penalty 0
busy_penalty 0

Control Type Assignment

Stop Signs

Currently stop sign designations are assigned at the intersection level, meaning if there is any stop sign at an intersection, all edges terminating at that intersection are assigned a stop sign penalty:

Pedestrian Infrastructure Assignment

Crosswalks

Crosswalk assignment currently works like stop sign assignment described above. If there is a crosswalk at an intersection, all edges terminating at that intersection are assigned a crosswalk penalty:

If OSM has footway edges representing the crosswalks, then those footways will be associated with the crosswalk, as seen in the right-most intersection above. Otherwise, the crosswalks will be associated with the roadway edges as seen in the two intersections to the left.

Bicycle Infrastructure Assignment

Bike Lanes

Bike infrastructure is assigned by converting LADOT Bikeways lines to points, and then snapping those points to the OSM network:

^ Above, LADOT Bikeways are shown in teal, with OSM ways shown in pink where they have been assigned bicycle infrastructure and blue where they have not.

Slope Computation

Examples

The following images show the LA county OSM roads colored from green to red based on the percentage of each OSM way that has a slope >= 6%:

  1. This county-wide map shows roads with the highest percentage of slopes >6% clustered around the the foothills of the Santa Monica and San Gabriel mountain ranges, as expected:

  2. A more detailed view shows the severity of the slopes of streets leading down to sea level near Manhattan Beach:

  3. A third image highlights the slopes of roads to the NW of Dodger Stadium, including the infamously inclined Baxter Street:

Land Use Data

The following datasets are used by Conveyal to define "opportunities" for computing accessibilities and are not required for computing generalized costs on the travel network:

  • Land Use - Additional land use data for use in Conveyal Analysis are available on the project sharepoint site. Their contents have been documented in the LADOT_landuse_data_inventory.xlsx file also on the sharepoint site.
  • Census - The script to generate Census-based population and household data stored as shapefiles is located in the scripts/ directory of this repository. The latest data as of March 2020 is included on the sharepoint site.