Purpose Congestion pricing policies (CPPs) are a travel demand management strategy designed to reduce peak-period traffic volumes by financially encouraging road users to use alternate transport modes, eliminate trips, or travel at different times. Several U.S. cities are considering CPPs, and New York City (NYC) plans to implement a CPP in 2021. While researchers have explored traffic flow and air pollution impacts of these policies, little is known about road safety impacts. We examined potential pedestrian safety impacts of NYCs proposed CPP.
Methods Integrating data from 1) multidisciplinary group model building sessions focused on pedestrian fatality trends, 2) a robust review of the CPP literature, and 3) NYC-specific data from the Metropolitan Transportation Authority, Department of Transportation, and American Community Survey, among others, we built a system dynamics simulation model with a time horizon of 20052035. The model structure represents alternate dynamic hypotheses about interrelated mechanisms underlying the impact of CPP on NYC mode-specific travel and pedestrian safety trends.
Results Key dynamic feedback mechanisms contributing to safety trends included demand for and, ultimately, use of specific travel modes (e.g., personal vehicle, metro, walking), which affected overall attractiveness of a given mode (e.g., due to congestion, crowding, perceived safety), and then further affected demand for corresponding modes. CPP revenue allocation to support use of specific modes by increasing numbers of metro cars and pedestrian/cyclist infrastructure may improve long-term safety trends, but delays in purchasing and improving infrastructure could negatively impact short-term safety trends.
Conclusions CPPs affect several, interrelated transportation and injury outcomes. Cities considering these policies should assess the status of and need for critical injury prevention strategies, such as safe and equitable infrastructure for all travelers, prior to implementation.
Contribution System dynamics models are an important tool for exploring the interrelated factors and ripple effects that determine transportation policy impacts.