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
As wildfires intensify across the western United States, communities in the wildland-urban interface face growing risks to life, infrastructure, and mobility. Wildfire evacuations, often occurring under rapidly changing conditions with limited warning, require coordinated responses informed by real-world human behavior. However, current evacuation planning models often rely on assumptions that do not reflect the variability
Individual travel behavior changes such as changing from driving to non-car-based modes of transportation can have overarching implications at the system level in reducing congestion and improving the mobility for all. And yet, individual behavioral changes are hard due to a variety of personal and system-level factors. Efforts to change individual travel behaviors have been
Opportunities and Limits of Individual Behavioral Changes: Phase I Read More »
Real-time origin-destination (OD) demand estimation is essential for urban transportation management, enabling responsive traffic control, dynamic transit scheduling, e-hailing services, and emergency operations. Traditional OD estimation methods rely heavily on traffic sensor data and statistical modeling; but the limited coverage of sensors in most cities poses a significant challenge. Emerging data sources—such as mobile trajectories
Real-Time Transportation Origin-Destination Demand Estimation Using Multimodality Data Read More »
People with disabilities disproportionately rely on public transportation to access employment, education, and healthcare services; however, public transit is not always available or equally distributed, which excludes social and community participation (Bascom & Christensen, 2017). Car transit is thus the only viable alternative. Since the Americans with Disability Act (ADA) of 1990, 4-8% of public
Ubiquitous mobile devices have resulted in massive amount of location- and time-stamped traces that can be used to infer people’s mobility patterns for various applications. Unlike household travel survey data that is small but rich (short but wide data), mobile data, is often massive but shallow (long but thin data) whose meanings in terms of
Imputing Socio-Demographics for Mobile Trajectors Read More »
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 »
In an era characterized by transformative shifts in demographics, lifestyles, work patterns, technological advances, societal values, and climate and environmental conditions, decision-makers are now confronted with ever-increasing, multifaceted uncertainties. The TBD National Center has launched a flagship initiative, called the TBD Hub, to provide transportation decision-makers information and deep insights about the state of the
Travel Behavior Data (TBD) Hub Read More »
Researchers have long examined the potential effects of telework on the geography of opportunity within metropolitan areas. While telework can increase access to certain job markets, it may also contribute to the decentralization of employment and population, fostering more spatially dispersed patterns of metropolitan growth. As jobs and services become more widely distributed, the central
