Understanding variations in activity-based vehicle allocation decisions: A latent segmentation-based random parameter logit modeling approach

摘要: Abstract This paper examines the activity-based vehicle allocation decisions among individuals during mandatory and discretionary activity-tours based on travel accompanying arrangements, specifically, solo (i.e. traveling alone) joint with household/non-household members). Latent segmentation-based random parameter logit (LSRPL) models are developed in this study to explore factors affecting within households, including characteristics, built environment accessibility measures. For instance, model results suggest that presence of children mandatory- increases individuals’ probability getting SUVs from their households’ existing fleet. Also, tour complexity identified by higher number activity stops, exhibits positive coefficient value for SUV case activity-tours. One unique features includes evaluating effects attitudes at tour-level. example, due attitude towards active transportation, likelihood allocating any decrease both tours. The however, vary across two segments. Older-higher income segment one tend get household fleet a activity-tour while living mixed land-use areas, younger-lower exhibit negative relationship SUVs. In addition heterogeneity segments, preference might same segment, as indicated statistically significant standard deviation ‘land-use index’ variable.