Latent Active Transportation Methodology

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Abstract
Understanding and estimating latent demand for active transportation, such as walking and cycling, is essential for designing infrastructure and policies that promote sustainable mobility. Unlike traditional demand models that focus on observed trips, latent demand estimation seeks to quantify the unrealized potential for active travel due to barriers such as inadequate infrastructure, safety concerns, or lack of connectivity. This study develops a comprehensive latent demand model tailored for California, integrating geospatial analysis and multimodal accessibility assessments. The methodology employs a GIS-based corridor analysis approach, utilizing spatial accessibility metrics and distance decay functions to evaluate potential demand. It incorporates employment and population data, school and university enrollments, and park and trail accessibility to estimate the likelihood of walking and cycling trips. To better capture behavioral patterns, the model also classifies cyclists into four categories: strong and fearless, enthused and confident, interested but concerned, and no way, no how. Case studies in Douglas City, El Centro, and downtown San Jose illustrate the model’s application across urban, suburban, and rural contexts. Findings highlight that employment centers and commercial areas significantly contribute to bicycle demand, while schools and recreational spaces influence pedestrian activity. The results emphasize the need for targeted infrastructure improvements to convert latent demand into realized active transportation. This study provides a data-driven framework to guide investments in pedestrian and cycling infrastructure to ensure equitable and effective transportation planning in California.

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