Where to Implement Leading Pedestrian Intervals: An Examination of Turning Vehicle–Pedestrian Crashes at Signalized Intersections

Abstract

Pedestrian safety is a critical transportation and public health issue, with fatalities increasing substantially over the past decade. Given this trend, it is important to understand where and when to most effectively implement countermeasures that help prevent pedestrian crashes, injuries, and fatalities. The leading pedestrian interval (LPI) is one such countermeasure which is considered relatively low-cost and has been shown to improve pedestrian safety. The LPI gives pedestrians a WALK indication 3–7 s before parallel vehicles receive a green indication at signalized intersections, potentially reducing the probability of turning vehicle–pedestrian crashes. LPI implementation may depend on many factors, and several implementation guidelines exist; however, these guidelines can vary from jurisdiction to jurisdiction, and weighting schemes may not always be based on empirical data. To address this issue, this study utilized roadway, traffic, and built environment data from 1,067 signalized intersections in Phoenix, Arizona, along with 2016–2022 crash data in developing a negative binomial regression model to analyze factors associated with turning vehicle–pedestrian crashes. Model results were compared with existing guidelines, and several variables commonly considered for LPI implementation were found to be significant predictors of turning vehicle–pedestrian crash frequency including activity density, major and minor road volumes, transit stop presence, proximity to schools, and number of intersection legs, among others, all at differing magnitudes. Ultimately, the relative predicted impacts of these characteristics may be useful to practitioners, researchers, and policymakers by providing data-driven evidence to help establish weighting schemes and other considerations for future iterations of LPI implementation guidelines. While the results of this study do not constitute a set of LPI guidelines on their own, the results can be useful to practitioners in multiple ways. First, the model results can be used to conduct an initial screening process to identify intersections with a high predicted frequency of turning vehicle–pedestrian or total pedestrian crashes based on intersection characteristics where an LPI (or other pedestrian-related countermeasures) could be considered after a more detailed study of the high-risk locations. Observed crashes at these intersections could also be considered in this process, but the use of model-predicted crashes allows for a more proactive approach to identify intersections that are high-risk based on their characteristics but may not have experienced pedestrian crashes in recent years by chance. Next, the results of the turning vehicle–pedestrian crash frequency model can be used to establish guidance or weighting schemes for intersection-level considerations when developing more comprehensive LPI guidelines. For example, intersections within 850 m (2,789 ft) from schools and sites within 91.4 m (300 ft) of a transit stop could be given higher weights when considering LPI implementation since the model results showed these sites to be significantly associated with higher predicted turning vehicle–pedestrian crash frequencies. Similar considerations could be given to vehicle volumes, gross activity density, and other intersection-level characteristics based on the model results. It is recognized, however, that certain crosswalk-level characteristics such as pedestrian and turning vehicle volumes and/or pedestrian-vehicle conflicts could also be considered when developing LPI guidelines and are not included as part of this study. Additionally, the impacts of LPIs on vehicular delay could also be considered, though higher priority should be given to pedestrian safety.