Stplanpy: A sustainable transportation planner for Python

Abstract

Among many other advantages, promoting commuting by bicycle can be used as a strategy to both reduce greenhouse gas emissions and improve public health. The sustainable transportation planner for Python, stplanpy, uses American community survey (ACS) origin–destination data to analyze bicycle commute patterns on the area, origin–destination, route, and network level. This includes both current patterns and patterns based on different (future) mode share scenarios. These scenarios can be used to identify the latent demand for bicycle infrastructure based on trip distance and hilliness, and to estimate greenhouse gas emission reductions and potential public health benefits. Using stplanpy to analyze bicycle commuting patterns in Palo Alto, CA it is found that due to long commuting distances, even Dutch levels of bicycling would not significantly reduce greenhouse gas emissions. However, the public health benefits for the residents of Palo Alto due to the adoption of an active lifestyle would be significant. Stplanpy is easy to install, comes with high quality documentation, is easy to use, and is open source.

Keywords

• Sustainable transportation
• Bicycling
• Travel mode shift
• Transportation network analysis
• Latent demand
• Emissions reduction