This paper presents a data collection framework and its prototype application for personal activity–travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-learning techniques to infer automatically activity types and travel modes with minimum interruption for the respondents. The three main components of the framework are (a) 24-h location data collection, (b) a dynamic land use database, and (c) a transportation mode identification component. The location logger is based on the smartphone network and can run for 24 h with minimal impact on smartphone battery life. The location logger is applicable equally in places where Global Positioning System signals are and are not available. The land use information is continuously updated from Internet location services such as Foursquare. The transportation mode identification module is able to distinguish six modes with 98.85% accuracy. The prototype application is conducted in the city of Toronto, Ontario, Canada, and the results clearly indicate the viability of this framework.