Mobility Behavior Segmentation for Personalized AMoD Service Design: Evidence from Israel

For decades, transportation planning has relied on utilitarian principles, which aim to maximize cumulative benefit by meeting the needs of the “average user.” This approach ignores fundamental differences between population groups and produces uniform solutions that fail to address the diverse needs of women, children, the elderly, and other disadvantaged populations. In response, there are growing calls for a transportation justice paradigm that emphasizes individuals’ ability to access meaningful opportunities according to their characteristics, abilities, and life circumstances. Autonomous Mobility on Demand (AMoD) holds the potential to transform future transportation systems. However, without deliberate planning, they risk replicating existing patterns of inequality for populations whose mobility needs differ from those of the average user. This study applies transportation justice principles to examine how AMoD systems can be designed to meet diverse user needs. Using a combination of an Autoencoder for learning reduced representations and an HDBSCAN clustering algorithm, the analysis identifies distinct travel patterns across socioeconomic groups. These findings reveal significant gaps between population segments, particularly among children and older adults, and demonstrate how AMoD systems could expand access to after-school activities, reduce social isolation among elderly women, and reduce various transportation-related social gaps by improving their ability to reach a wider range of opportunities.