Travel behavior data enable the understanding of why, how, and when people travel, and play a critical role in travel trend monitoring, transportation planning, and policy decision support. Conventional travel behavior data collection methods such as the National Household Travel Survey (NHTS) have been the primary source of travel behavior information for transportation agencies. However, the relatively high cost of traditional travel surveys often prohibits frequent survey cycles (currently once every 5-10 years). With decision makers increasingly requesting recent and up-to-date information on multimodal travel trends, establishing a sustainable and timely travel monitoring program based on available data sources from the public domain is in order. This dissertation developed advanced data processing, expansion, fusion and analysis methods and integrated such methods with existing public domain data into a comprehensive model that allows transportation agencies to track monthly multimodal travel behavior trends, e.g., mode share, number of trips, and trip frequency, at the metropolitan level.

Advanced data analytical methods are developed to overcome significant challenges for tracking monthly travel behavior trends of different modes. The proposed methods are tailored to address different challenges for different modes and are flexible enough to accommodate heterogeneous spatial and temporary resolutions and updating schedules of different data sources.

For the driving mode, this dissertation developed reliable methods for estimates of local road VMT, various temporal adjustment factors, truck percentage factors, average vehicular occupancy, and average trip length based on additional data from the Travel Monitoring Analysis System and the most recent regional household travel survey to translate HPMS data into monthly number of vehicular and person driving trips for a metropolitan area.

For the transit mode, this dissertation collectively exhausted detailed transit network geo-data to complement NTD and developed advanced geo-analysis and statistical methods tailored to the service network of different types of operators to accurately and reliably allocate ridership data to the metropolitan area of interest, and to allocate annual ridership data to each month.

The data for non-motorized is even more sparse, although the local government has growing interests and efforts on collecting such data. A two-step statistical model is developed to derive the trend for non-motorized modes and then integrating such trends with base-year number of trips number from most recent household travel survey conducted in the metropolitan areas of interest.

Based on the number of trips by modes estimated using the proposed methods, the monthly trend in mode share can be timely estimated and continuously monitored over time for the first time in the literature using public domain data only.

The dissertation has demonstrated that it is feasible to develop a comprehensive model for multimodal travel trend monitoring and analysis by integrating a wide range of traffic and travel behavior data sets of multiple travel modes. Based on findings, it can be concluded that the proposed public-domain databases and data processing, expansion, fusion and analysis methods can provide a reliable way to monitor the month-to-month multimodal travel demand at the metropolitan level across the U.S.