Integrating complex systems science into road safety research and practice, Part 2: applying systems tools to the problem of increasing pedestrian death rates

Abstract

Objectives: To provide a specific example of how systems dynamics tools can increase understanding of stakeholder 'mental models' and generate robust systems-based hypotheses about the escalating problem of rising pedestrian death rates in the USA.

Methods: We designed and facilitated two group model building (GMB) workshops. Participants generated causal loop diagrams (CLDs) individually and in small groups to explore hypotheses concerning time-dynamic interacting factors underlying the increasing rates of pedestrian deaths. Using a grounded theory approach, research team members synthesised the structures and hypotheses into a single CLD.

Results: CLDs from the 41 participants indicated four core factors hypothesised to have a direct impact on pedestrian fatalities: pedestrian-vehicle crashes, vehicle speed at the time of the crash, vehicle size/dimensions and emergency response time. Participants diagrammed how actions and reactions impacted these proximal factors over time and led to ripple effects throughout a larger system to generate an increase in pedestrian deaths. Hypothesised contributing mechanisms fell within the following broad categories: community responses; research, policy and industry influence; potential unintended consequences of responses to pedestrian deaths; and the role of sprawl.

Conclusions: This application of systems science tools suggested several strategies for advancing injury prevention research and practice. The project generated robust hypotheses and advanced stakeholder communication and depth of understanding and engagement in this key issue. The CLD and GMB process detailed in this study provides a concrete example of how systems tools can be adopted and applied to a transportation safety topic.

Keywords: injury prevention; pedestrian; road safety; system dynamics; systems science.

FIGURE 1.

Count and rate of pedestrian deaths in the United States, 2009–2017 Source: National Highway Traffic Safety Administration, Fatality Analysis Reporting System

FIGURE 2.

Synthesized causal loop diagram of hypothesized community-level system structure driving pedestrian death rates over time ped= pedestrian; || represents hypothesized time delay; + represents hypothesis that a connected pair of variables change in the same direction over time (e.g., as one variable increases the other variable increases as well); - represents hypothesis that a connected pair of variables change in the opposite direction over time (e.g., as one variable increases the other variable decreases, and vice versa); B# represent numbered balancing loops; R# represent numbered reinforcing loops.

FIGURE 3.

Synthesized causal loop diagram of hypothesized system structure involving factors external to communities that may be driving increases in driving pedestrian death rates over time. B, balancing; Ped, pedestrian; R, reinforcing; VMT, vehicle miles travelled.

FIGURE 4.

Synthesized causal loop diagram of hypothesized system structure involving factors related to regional growth and vehicle miles traveled (VMT) that may be driving increases in pedestrian death rates over time. B, balancing; Ped, pedestrian; R, reinforcing; VMT, vehicle miles travelled.