published journal article
published journal article
MS Thesis
With the emergence of connected vehicle technologies, many interesting applications become possible. We look at one such application that allows trac system users with heterogeneous values of time (VOT) to trade their right of way (ROW) among other users so as to minimize individuals’ total travel costs, which includes time cost and nancial transaction. Our proposed system allows users on a multi-lane road to trade their ROWs, and the resulting traffic flow violates the First-In-First-Out (FIFO) principle, since vehicles with higher VOTs would travel faster by paying those with lower VOTs. A novel multi-commodity kinematic wave model is developed for such a system based on the assumptions of choice, uniability, prices, budget-balance, utility maximization, referred to as A1 through A5. From these assumptions, we study a simple case of such a system in which two groups of users with different VOTs participate in the scheme, and a third group does not participate. We derive a uniable fundamental diagram in which the relative speed ratios of different commodities are constant and proportional to the square root of the VOTs. We show that the scheme is Pareto-improving at the system optimal solution. The Riemann Problem is solved both analytically and numerically for this system, and we demonstrate that different commodities (user groups) would react differently to shockwaves and rarefaction waves. The study is concluded with future extensions.
published journal article
published journal article
conference paper
published journal article
published journal article
Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018–2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 μg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities.
conference paper
Connected Vehicle (CV) technologies are under rapid deployment across the globe and will soon reshape our transportation systems, bringing benefits to mobility, safety, environment, etc. Meanwhile, such technologies also attract attention from cyberattacks. Recent work shows that CV-based Intelligent Traffic Signal Control Systems are vulnerable to data spoofing attacks, which can cause severe congestion effects in intersections. In this work, we explore a general detection strategy for infrastructure-side CV applications by estimating the trustworthiness of CVs based on readily-available infrastructure-side sensors. We implement our detector for the CV-based traffic signal control and evaluate it against two representative congestion attacks. Our evaluation in the industrial-grade traffic simulator shows that the detector can detect attacks with at least 95% true positive rates while keeping false positive rate below 7% and is robust to sensor noises.
published journal article