published journal article
published journal article
Government transparency is generally uncontroversial, intuitively appealing, and held to be a cornerstone of planning practice. This article systematically reviews planning scholars’ treatment of government transparency in the twenty-first century. We find that transparency frequently underpins key theoretical constructs and policy prescriptions, but scholars rarely define or operationalize the term and generally treat it as unproblematic. We then identify how transparency requirements can conflict with the goals of accountability, participation, and inclusion, and we conclude by discussing the implications for assessing the role of transparency in social change.
Phd Dissertation
Trucks contribute disproportionally to traffic congestion, emissions, road safety issues, and infrastructure and maintenance costs. In addition, truck flow patterns are known to vary by season and time-of-day as trucks serve different industries and facilities. Therefore, truck flow data are critical for transportation planning, freight modeling, and highway infrastructure design and operations. However, the current data sources only provide partial truck flow or point observations. This dissertation developed a framework for estimating path flows of trucks by tracking individual vehicles as they traverse detector stations over long distances. Truck physical attributes and inductive waveform signatures were collected from advanced point detector systems and used to match vehicles between detector locations by a Selective Weighted Bayesian Model (SWBM). The key feature variables that were the most influential in distinguishing vehicles were identified and emphasized in the SWBM to efficiently and successfully track vehicles across road networks. The initial results showed that the Bayesian approach with the full integration of two complementary detector data types – advanced inductive loop detectors and Weigh-in-Motion (WIM) sensors – could successfully track trucks over long distances (i.e., 26 miles) by minimizing the impacts of measurement variations and errors from the detection systems. The network implementation of the model demonstrated high coverage and accuracy, which affirmed the capability of the tracking approach to provide comprehensive truck travel patterns in a complex network. Specifically, the model was able to successfully match 90 percent of multi-unit trucks where only 67 percent of trucks observed at a downstream site passed an upstream detection site. A strategic plan to identify optimal sensor locations to maximize benefits from the truck tracking model was also proposed. A decision model that optimally locates sensors to capture the maximum truck OD and route flow was investigated using a goal programming approach. This approach suggested optimal locations for tracking implementation in a large truck network considering a limited budget. Results showed that sensor locations from a maximum-flow-capturing approach were more advantageous to observe truck flow than a conventional sensor location approach that focuses on OD and route identifiability.
working paper
Transit performance can be evaluated through quantitative indicators. As the provision of efficient and effective transit service are appropriate goals to be encouraged by federal and state governments, these goals are used to develop performance indicators. Three efficiency and four effectiveness indicators are described, together with two overall indicators. These nine indicators are analyzed for comparability utilizing operating and financial data collected from public transit agencies in California. Performance indicators selected for this study should not be viewed as final. Twenty-one performance indicators proposed by previous studies were reviewed. Theoretical considerations and unavailability or unreliability of data caused omission of several useful measures like passenger-miles. Circumstances such as improved data, emphasis upon goals other than efficiency and effectiveness, and local conditions might warrant the inclusion of indicators deleted from this research.
research report |
Dec 1977
published journal article |
Dec 1978
published journal article
Skin diseases have become a global concern. This study aims to evaluate the associations between ambient air pollution and emergency room visits for skin diseases under the background of improving air quality in China. Based on 45,094 cases from a general hospital and fixed-site monitoring environmental data from 2014–2019 in Beijing, China, this study used generalized additive models with quasi-Poisson regression to estimate the exposure–health associations at lag 0–1 to lag 0–7. PM2.5 and NO2 exposure were associated with increased emergency room visits for total skin diseases (ICD10: L00-L99). Positive associations of PM2.5, PM10, O3 and NO2 with dermatitis/eczema (ICD-10: L20–30), as well as SO2 and NO2 with urticaria (ICD-10: L50) visits were also found. For instance, a 10 μg/m3 increase in PM2.5 was associated with increases of 0.7% (95%CI: 0.2%, 1.2%) in total skin diseases visits at lag 0–5 and 1.1% (95%CI: 0.6%, 1.7%) in dermatitis/eczema visits at lag 0–1, respectively. For PM2.5, PM10 and CO, stronger annual associations were typically observed in the high-pollution (2014) and low-pollution (2018/2019) years. For instance, a 10 μg/m3 increase in PM2.5 at lag 0–5 was associated with increases of 1.8% (95%CI: 1.0%, 2.6%) and 2.3% (95%CI: 0.4%, 4.3%) in total skin disease visits in 2014 and 2018, respectively. Our study emphasizes the necessity of controlling the potential health hazard of air pollutants on skin, although significant achievements in air quality control have been made in China.
published journal article
PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities.
published journal article
Phd Dissertation
research report
The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California.View the NCST Project Webpage
published journal article