Mode Choice Heterogeneity Among Zero-Vehicle Households: A Latent Class Cluster Approach

2025-12-05

AI Summary

Traditional transportation planning treats zero-vehicle households (ZVHs) as a homogeneous group, overlooking inherent heterogeneity in their travel behaviors. To address this gap, this study develops, for the first time, a weighted latent class cluster analysis (LCCA) model using 2022 National Household Travel Survey data. The model jointly incorporates travel mode choice, trip purpose, and multidimensional covariates—including sociodemographic, economic, and built-environmental factors—to identify three distinct ZVH traveler segments: shared-mobility commuters (36.3%), car-borrowing shoppers (29.9%), and active-mobility shoppers (33.8%). Results challenge the prevailing assumption that ZVHs are uniformly passive dependents, revealing instead strategic adaptation and behavioral differentiation. This work advances both empirical understanding and methodological practice, providing a rigorous foundation for equitable, behaviorally informed transportation policy design.

Technology Category

• Application Domains: Transportation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Data transparency and provenance

Abstract

In transportation planning, Zero-Vehicle Households (ZVHs) are often treated as a uniform group with limited mobility options and assumed to rely heavily on walking or public transit. However, such assumptions overlook the diverse travel strategies ZVHs employ in response to varying trip needs and sociodemographic factors. This study addresses this gap by applying a weighted Latent Class Cluster Analysis (LCCA) to data from the 2022 National Household Travel Survey (NHTS) to uncover distinct mobility patterns within the ZVH population. Using travel mode and trip purpose as indicators and demographic, economic, and built environment variables as covariates, we identified three latent classes :Shared mobility errand workers (36.3%), who primarily use transit and ridehailing for commuting and essential activities; car based shoppers (29.9%), who depend on informal vehicle access for longer discretionary trips and active travel Shoppers (33.8%), who rely on walking or cycling for short, local shopping oriented travel. These behavioral findings enable policymakers to develop differentiated planning solutions to the specific needs of each segment among the ZVHs population across varied geographic and demographic settings.

Problem

• Identifies distinct mobility patterns within zero-vehicle households.
• Challenges the assumption of uniform travel behavior in this population.
• Enables targeted transportation policies for different ZVH segments.

Innovation

• Latent Class Cluster Analysis identifies mobility patterns
• Weighted LCCA uses travel mode and trip purpose indicators
• Demographic and built environment variables as covariates