Statement of Purpose Congestion pricing policies (CPPs) are a strategy designed to reduce vehicle volumes by financially encouraging road users to use alternate transport modes or eliminate or shift trips. Research on CPP impacts has generally focused on economic and traffic flow benefits. Few studies have examined safety effects for vulnerable road users, a critical outcome given increasing pedestrian injury trends. The objective of this study was to examine how a CPP might impact pedestrian injury trends in Manhattan, NY.

Methods/Approach We built a system dynamics (SD) simulation model to explore the potential mechanisms producing pedestrian injuries over time and the impacts of a CPP (and related interventions) on this trend. SD model development and analysis involved five steps: 1) causal loop diagram development (with input from > 75 stakeholders); 2) creation and refining of SD simulation model structure; 3) collection and triangulation of several data sources; 4) model verification, validation, and calibration; and 5) policy analyses.

Results We found that CPP impacts on pedestrian injury trends varied based on important decisions related to how the CPP was designed. CPPs with complementary infrastructure improvements and speed management interventions could help cities achieve both congestion-relieving goals while also improving safety, resulting in 3–20% reductions in cumulative injuries between 2020 and 2030, as compared to no CPP implementation. Additionally, certain CPP configurations (e.g., additional charges on for-hire vehicles) could further reduce daily vehicle trips and congestion but might lead to unintended negative consequences of greater pedestrian injuries.

Significance This is the first model to provide a holistic and endogenous look at how interconnected processes affecting congestion and CPP impacts also affect vulnerable road user safety. The use of SD models can facilitate a holistic inspection of potential intended and unintended effects across a range of safety and other outcomes, prior to policy implementation.