Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the richest perspective on the study of inter- and intrauser behavioral variations. Yet after over a decade of research and field experimentation on such surveys, and despite a consensus in transportation research as to their potential, smartphone-based travel surveys are seldom used on a large scale. This literature review pinpoints and examines the problems limiting prior research, and exposes drivers to select and rank machine-learning algorithms used for data processing in smartphone-based surveys. Our findings show the main physical limitations from a device perspective; the methodological framework deployed for the automatic generation of travel-diaries, from the application perspective; and the relationship among user interaction, methods, and data, from the ground truth perspective.