Advocacy with Mobility Data: Making Streets Safer for Micromobility 

Abstract
Evidence suggests that micromobility can contribute to increased mobility, reduced greenhouse gas emissions, decreased automobile use, economic development, and health benefits (1-4). In 2020, Spin and Ford Motor Company launched the Mobility Data for Safer Streets (MDSS) initiative, which aimed to make streets safer for micromobility by awarding six mobility advocacy organizations around the country with a suite of software- and hardware-based data tools to be deployed in support of street transformation projects in their city. The awardees were Bike Utah, WalkBike Nashville, Bike Cleveland, WalkBike North Carolina, Sustain Charlotte, and Denver Streets Partnership. MDSS provided awardees access to Streetlight Data, Populus, Gehl’s Public Space Public Life (PSPL) Digital Survey Toolkit, Urban Footprint, and Strava Metro local data, as well as a radar speed gun, bike-ped counter and time-lapse camera. Using these tools, advocates gathered critical data to make the case for renewed and reimagined infrastructure that was safer and that could help correct racial and economic inequities.

This research reports findings from in-depth interviews with awardees focusing on how they used the tools to gather, analyze, and present more data-driven recommendations and how these recommendations have already inspired change, even given the constraints the COVID-19 pandemic has imposed. The goals of this paper are threefold: First, we highlight the user experiences of awardees with regard to the tools themselves, offering insights into use cases for and usability of mobility data tools. Second, we discuss how the MDSS initiative and the data tools themselves address mobility equity issues, with regard to access to active transportation facilities and shared micromobility in underserved communities as well as data issues related to equity. We conclude by articulating some best practices for leveraging new mobility data tools to drive planning and policy changes, leading to more equitable, efficient, and micromobility-friendly cities.

Common use cases awardees found for the data tools include documenting traffic speeds and volumes to advocate for road diets or traffic calming interventions, and conducting pre-post analyses of interventions such as new bike facilities and slow streets. Usability strengths of the data tools included rich data, complementarity among multiple tools, and good customer support. Usability challenges included learnability and data validity concerns. Multiple awardees focused on needs of underserved communities, finding troubling correlations between race and income and access to micromobility infrastructure. We will discuss how these inequities are often “hidden” because of gaps in available data (prior to the MDSS initiative). Our discussion of use cases, including those related to equity issues, will include and reference data outputs generated by the awardees (e.g., maps, graphs, tables).