Level of Traffic Stress (LTS) Calculation

This project calculates Level of Traffic Stress (LTS) scores on the DVRPC model network. It also includes a component with conflating speed data from Overture Maps to help with more accurate scoring.

Overview

The workflow:

1. Sets up a PostgreSQL/PostGIS database
2. Loads Overture Maps transportation segment data
3. Loads DVRPC model network shapefile
4. Conflates speed data from Overture to model network
5. Calculates LTS scores (1-4)
6. Cleans up temporary files

Prerequisites

• PostgreSQL with PostGIS extension
• Python 3.9+
• Required tools: psql, ogr2ogr

Setup

1. Install Python dependencies:

pip install -r requirements.txt

2. Create a .env file with your configuration:

# PostgreSQL Connection
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_USER=postgres
POSTGRES_PASSWORD=your_password
POSTGRES_DB=lts

# Overture Maps Configuration
OVERTURE_VERSION=2025-11-19.0

# Overture Maps download bounding box (west, south, east, north)
BBOX_WEST=-76.210785
BBOX_SOUTH=39.478606
BBOX_EAST=-73.885803
BBOX_NORTH=40.601963

# Model Network Shapefile (without .shp extension)
BIKE_NETWORK_SHAPEFILE=input/bike_network_dec1_2025_link

3. Place your model network shapefile in the input/ directory and update NETWORK_SHAPEFILE in .env

Required Model Network Shapefile Fields

Your shapefile must contain these fields:

• no (numeric) - Segment ID
• fromnodeno (numeric) - From node ID
• tonodeno (numeric) - To node ID
• typeno (varchar) - Road type code (72, 79 for residential)
• numlanes (numeric) - Number of lanes
• bike_fac~1 (numeric) - Bike facility type (0-9)
• vcur_prt~2 (varchar) - Speed field (e.g., "25mph")
• geom - Geometry (LineString)

Usage

Run the complete workflow:

python3 run.py

Output

Final table: output.model_network_with_lts

Contains all original bike network fields plus:

• speed_mph: Speed limit (from Overture or original data)
• posted_speed: Speed from Overture conflation (if available)
• lts: Level of Traffic Stress score (1-4, or -1/-2 for special cases)

Files

• run.py - Main workflow script
• conflate_speed_data.sql - Spatial conflation logic
• calculate_lts.sql - LTS calculation using DVRPC methodology
• old_lts.py - Reference: Original LTS methodology from Visum
• requirements.txt - Python dependencies
• .env - Database configuration (create this)

Bike Facility Codes

The bike_fac~1 field should use these codes:

• 0 - No bike facility
• 1 - Bike lane
• 2 - Buffered bike lane
• 3 - Protected bike lane / cycle track
• 4 - Shared lane markings (sharrows)
• 5 - Paved shoulder
• 6 - Separate path
• 9 - Opposite direction of one-way street

Notes

• The overture_roads.geojson file (1.7GB) is automatically removed after loading to save space
• Speed data is preferentially taken from Overture Maps, falling back to original vcur_prt~2 field
• The conflation uses spatial matching with 10m buffer and bearing alignment (±20°)