This study describes the development and validation of pedestrian intersection crossing volume models for the seven-county Milwaukee metropolitan region. The set of three models, among the first developed at a multi-county scale, can be used to estimate the total number of pedestrian crossings per year at four-leg intersections along state highways and other major thoroughfares. Outputs are appropriate for annual volumes ranging from 1,000 to 650,000. We used negative binomial regression to relate annual pedestrian volumes at 260 intersections to roadway and surrounding neighborhood socioeconomic and land-use variables. The three models include seven variables that have significant positive associations with annual pedestrian volume: population density within 400 m of the intersection; employment density within 400 m; number of bus stops within 100 m; number of retail businesses within 100 m; number of restaurant and bar businesses within 100 m; presence of a school within 400 m; and proportion of households without a motor vehicle within 400 m. Results suggest that square root or cube root transformations of continuous explanatory variables could potentially improve model fit. The models have fair accuracy, with each of the three model formulations predicting 60% or more of validation intersection counts to within half or double the observed value. Future research could address overprediction by creating new variables to better represent the number of lanes on each intersection leg and low socioeconomic status of adjacent neighborhoods.

Two broad categories of barriers to improving pedestrian and bicycle transportation are concerns about traffic safety and personal security. Gathering residents’ perceptions of these barriers can help public agencies develop effective and equitable strategies to create more sustainable transportation systems. We analyzed open-ended responses to the 2020 Milwaukee Safe and Healthy Streets survey to identify common traffic safety barriers (e.g., driver behavior such as speeding and red-light-running) and personal security barriers (e.g., undesirable street behaviors such as gun violence, robbery, and assault) to walking and bicycling. Then, we developed binary logistic models to identify perceptions of neighborhood characteristics, and individual demographic characteristics related to perceiving walking or bicycling as unsafe with respect to traffic or personal security. For walking, respondents’ traffic safety concerns were most strongly associated with perceptions of fast neighborhood traffic speeds, and personal security concerns were associated with perceptions of poor neighborhood cleanliness. For bicycling, both traffic safety concerns and personal security concerns were most strongly associated with poor neighborhood opportunities for exercise. At an individual level, living in a zero-vehicle household and having self-reported poor health were associated with rating traffic safety for both walking and bicycling as unsafe; having disabilities was associated with rating walking as unsafe. In almost every aspect of our analysis, respondents living in lower-income communities reported greater barriers to pedestrian and bicycle safety and security than residents from wealthier neighborhoods. The results emphasized the importance of both the social and physical environment for improving pedestrian and bicycle transportation.

Multi-use trails are popular for transportation and recreation, but pedestrians and bicyclists are exposed to motor vehicle traffic at trail crossings (locations where trails cross roadways), creating the risk of crashes, injuries, and fatalities. Many trail crossing design guidelines suggest best practices to make trail crossings safe, but few studies have quantified the statistical relationship between trail user crashes and a broad set of trail crossing characteristics. Our study developed one of the first trail crossing crash models using trail user crashes reported at 197 crossings in the city of Minneapolis, MN, and in the Milwaukee, WI, region between 2011 and 2018. We took advantage of widespread trail counting programs and historic aerial and street-level imagery to create and test more than 30 theoretically important potential explanatory variables. We addressed the challenge that many crossings had small numbers of crashes (or zero crashes) during the study period by using a Poisson-lognormal model. Our model showed significant associations between trail crossing crashes and trail traffic volume, roadway motor vehicle volume, three-way intersections where the trail crosses perpendicular to the mainline roadway, and total crossing length. Although not statistically significant, signalized intersections and limited sight lines between drivers and trail users near crossings may also be associated with more crashes. Future research can build on this study and expand systemic efforts to improve trail crossing safety.

Research offers ample insights into how people of different genders could experience transportation systems in equitable ways, but gender equity is still not part of mainstream transportation practice. We propose that Complete Streets could serve as an implementation system to advance gender equity. We provide empirical information, gender concepts, and regional cases from literatures on gender and transportation, multimodal travel, and public space to support this call to action. We find that a gender-aware Complete Streets movement would: 1) implement gender-specific tools and data; 2) address social environments and infrastructure; and 3) establish a gender-inclusive agenda to reform transportation policy.