Household vehicle ownership is the foundation of personal mobility for the vast majority of American families. The number of vehicles a household owns, and the specific types of vehicles in its fleet, determine how household members access employment, healthcare, education, and services, and directly generate the travel demand that shapes roadway utilization, network performance, and
Expanding transportation data availability is creating new opportunities for a richer, potentially more representative, and broader data landscape to support the measurement and modeling of evolving travel behaviors and outcomes. This project addresses the need for clearer guidance on when, where, and how multiple data sources can be integrated to overcome common data limitations in
The average age of vehicles on U.S. roads has reached a record high of 12.6 years, indicating that people are holding on to cars longer than ever before retiring them. This project will investigate how vehicle attributes, age, cumulative mileage, and ownership and maintenance history affect consumers’ decisions to sell or retire vehicles. Understanding the
Determinants of Vehicle Holding Time and Scrappage Read More »
Estimating demands to points of interests (POI) involves predicting the number of visitors to specific locations, such as restaurants, retail stores, parks, or cultural sites like museums. Unlike traditional travel demand models, which focus on large zones (e.g., Transportation Analysis Zones (TAZ) or census tracts) for long-term planning such as transit network, POI visits estimation
Quasi-sparsity (QS) indicates that for a large-scale transportation network, most origin-destination (OD) demands are concentrated on a small fraction of the OD pairs, while majority of the OD pairs exhibit small (maybe non-zero) travel demands. One example is the King County network (the area that includes the City of Seattle in the State of Washington):
Quasi-Sparsity in Transportation Origin-Destination Demand Read More »
