Populations and land development have the potential to shift as economies change at a rate that is faster than currently employed for updating a transportation plan for a region. This thesis uses the Foursquare location-based social networking check-in data to analyze the origin-destination travel demand for Austin, Texas. A doubly-constrained gravity model has been employed to create an origin-destination model. This model was analyzed in comparison to a singly-constrained gravity model as well as the Capital Area Metropolitan Planning Organization's 2010 Urban Transportation Study's origin-destination matrices through trip length distributions, the zonal origin-destination flow patterns, and the zonal trip generation and attraction heat maps in an effort to validate the methodology. / text

This research contributes to a very active body of literature on the application of Automated Data Collection Systems (ADCS) and openly shared data to public transportation planning. It also addresses the interaction between transit demand and land use patterns, a key component of generating time-varying origin-destination (O-D) matrices at a route level. An origin-destination (O-D) matrix describes the travel demand between two different locations and is indispensable information for most transportation applications, from strategic planning to traffic control and management. A transit passenger's O-D pair at the route level simply indicates the origin and destination stop along the considered route. Observing existing land use types (e.g., residential, commercial, institutional) within the catchment area of each stop can help in identifying existing transit demand at any given time or over time. The proposed research addresses incorporation of an alighting probability matrix (APM) - tabulating the probabilities that a passenger alights at stops downstream of the boarding at a specified stop - into a time-varying O-D estimation process, based on the passenger's trip purpose or activity locations represented by the interactions between transit demand and land use patterns. In order to examine these interactions, this research also uses a much larger dataset that has been automatically collected from various electronic technologies: Automated Fare Collection (AFC) systems and Automated Passenger Counter (APC) systems, in conjunction with other readily available data such as Google's General Transit Feed Specification (GTFS) and parcel-level land use data. The large and highly detailed datasets have the capability of rectifying limitations of manual data collection (e.g., on-board survey) as well as enhancing any existing decision-making tools. This research proposes use of Google's GTFS for a bus stop aggregation model (SAM) based on distance between individual stops, textual similarity, and common service areas. By measuring land use types within a specified service area based on SAM, this research helps in advancing our understanding of transit demand in the vicinity of bus stops. In addition, a systematic matching technique for aggregating stops (SAM) allows us to analyze the symmetry of boarding and alightings, which can observe a considerable passenger flow between specific time periods and symmetry by time period pairs (e.g., between AM and PM peaks) on an individual day. This research explores the potential generation of a time-varying O-D matrix from APC data, in conjunction with integrated land use and transportation models. This research aims at incorporating all valuable information - the time-varying alighting probability matrix (TAPM) that represents on-board passengers' trip purpose - into the O-D estimation process. A practical application is based on APC data on a specific transit route in the Minneapolis - St. Paul metropolitan area. This research can also provide other practical implications. It can help transit agencies and policy makers to develop decision-making tools to support transit planning, using improved databases with transit-related ADCS and parcel-level land use data. As a result, this work not only has direct implications for the design and operation of future urban public transport systems (e.g., more precise bus scheduling, improve service to public transport users), but also for urban planning (e.g., for transit oriented urban development) and travel forecasting.