Case study demonstrates a data-driven method to assess infrastructure and pedestrian safety in low-income neighborhoods, highlighting urban planning implications.
Purpose This study aims to support the integration of micromobility systems such as shared bicycles and e-scooters into underserved communities by developing a systematic evaluation tool to assess infrastructure readiness and safety around transit stops. Design/methodology/approach A three-phase, mixed-methods framework was applied. A GIS-based analysis identified low-income neighborhoods with limited access to vehicles. A strategic bus stop in an underserved area was selected, and a surrounding buffer zone was assessed using the newly developed Micromobility Infrastructure Score Sheet (MISS). This tool evaluated physical infrastructure, including off-street and on-street facilities, crosswalks and bus stop amenities. Additionally, crash data from the Texas Crash Records Information System was analyzed to identify pedestrian and cyclist safety risks. Findings The MISS-safety analysis framework effectively synthesized infrastructure and safety data, highlighting specific gaps around the selected bus stop. These findings informed a preliminary micromobility system design tailored to the needs of the transit-dependent neighborhood. Research limitations/implications The MISS tool, while systematically developed, has not yet been validated against micromobility user experience data. Its scoring rubrics may need refinement based on real-world usage patterns and rider feedback. Originality/value The research has demonstrated a novel MISS-safety analysis framework that is transferable and integrates safety and infrastructure data to perform transit access analysis. This study extends existing micromobility planning approaches and offers a data-driven method that addresses infrastructure gaps to enhance multimodal connectivity in transit-dependent neighborhoods- an area not extensively examined in current literature. Moreover, the framework’s structured scoring approach makes it highly suitable for adoption by city planners and transportation practitioners seeking to assess and prioritize micromobility infrastructure around transit stops.
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Zaheen et al. (2025) studied this question.
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