The transportation sector is one of the largest contributors to greenhouse gas emissions and accounts for nearly 45% of global oil demand, primarily due to the widespread use of internal combustion engine vehicles (ICEVs). In fact, while the United States has seen overall reductions of greenhouse gas emissions of around 3% since 1990, emissions in […]
A Model of EV Adoption and Rank-Based Contributing Factors Read More »
The world has seen a surge in extreme weather events and increased challenges of disaster response. One overlooked factor complicating the distribution of relief supplies is the immediate decisions survivors make when seeking aid, which impacts the realized demand at various points of distribution (PODs) of relief supplies. This project aims to understand these critical
The primary objective of the project is to systematically nudge communities towards societal travel goals of mobility, accessibility, environmental sustainability, and equity by addressing the challenges of low adoption of sustainable travel modes (e.g., transit, walking, biking) and limited access to societal services/activities (e.g., jobs, medical, grocery stores) for disadvantaged groups (e.g., travelers in transit
A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation
TSP seeks to optimize the interaction between busses and the infrastructure, creating a minimum resistance path for transit buses through signalized intersections. TSP may improve travel time reliability (TTR), schedule adherence, and ultimately the quality of service and ridership for transit systems. Bhat and Sardesai explicitly includes of TTR in mode choice, demonstrating the significance
Improving Mobility Options through Transit Signal Priority (TSP) Read More »
The main objectives of this project are: (1) development and validation of detection methods on the presence and width of individual elements of complete streets at street level, (2) development of a numeric index and typology to rate the completeness of streets, (3) curation of a publicly accessible database of various elements of complete street
This project stands at the intersection of cognitive psychology, AI and computer vision, and transportation safety and efficiency. By focusing on the nuanced ways in which humans allocate their visual attention, and how this can inform the development of artificial intelligence (AI) and machine learning (ML) to aid in self-driving cars, transportation safety automation, and
Exploring Top-Down Visual Attention for Transportation Behavior Analysis Read More »
Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network
This project will use qualitative and quantitative research methods to better understand mobility-related challenges for Asian Americans and Pacific Islanders (AAPI) older adults in order to provide government agencies and organizations such as National Asian Pacific Center on Aging (NAPCA) with recommendations for policy and program changes to pursue. Older adults are typically defined as
Addressing Mobility-Related Challenges for AAPI Older Adults Read More »
Meeting the diverse needs of stakeholders such as passengers, drivers, and service providers is imperative. Modern travelers seek real-time updates and personalized journey experiences. Drivers need consolidated data for safety and punctuality (Chen et al., 2021), while service providers rely on data analytics to optimize resources and enhance reliability (Wang et al., 2020). Traditional centralized
