Spatial Visualization, Attitudes Toward Mathematics, And Mathematics Achievement Among Chinese-American, Hispanic-American, And Caucasian Seventh And Eighth Grade Students

Many studies have shown that spatial visualization and attitudes toward mathematics are positively and significantly correlated to achievement in mathematics. This study attempted to find out whether these relationships remain consistent across various ethnic groups. This study also attempted to ascertain if spatial visualization ability and attitudes toward mathematics vary among ethnic groups, and if these possible variabilities correspond to the different degrees of mathematical achievement. One hundred five 7th and 8th grade Caucasian, Chinese-American, and Hispanic-American students were selected from three of the five middle schools in the Stockton Unified School District to participate in this study. The DAT Space Relations Test, the Fennema-Sherman mathematics Attitude Scales, and the Comprehensive Tests of Basic Skills were administered to the students in the Spring of 1985 to assess spatial visualization ability, attitudes toward mathematics, and achievement in mathematics, respectively. The results indicated that Chinese-American students achieved significantly higher than Caucasian and Hispanic students in mathematics. The results of this study suggested that when English proficiency and family-income levels are controlled, Hispanic students (males and females combined) did not achieve at a significantly lower level than did Caucasian students as suggested in previous studies. Also when all three ethnic groups were combined, males achieved significantly higher than did females in mathematics. The data of the spatial visualization variable in this study indicated that Chinese-American males scored at a significantly higher level than did Chinese-American females. There was no significant sex difference in Caucasian and Hispanic groups. Students of both gender and all ethnic groups showed strongly positive attitudes toward mathematics. There were very few significant sex differences or ethnic differences in attitudes toward mathematics. There was a substantial correlation between spatial visualization and mathematics achievement. When all three ethnic groups were combined, females had a significantly higher correlation between mathematics achievement and spatial visualization than did males. Spatial visualization, ethnicity, and confidence of learning mathematics were significant predictors of mathematics achievement for the student population of this study

Short-Term Travel Time Prediction on Freeways

Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology’s advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models

Global Modeling and Analysis of Anthropogenic Combustion and Associated Emissions

Anthropogenic combustion and associated emissions have significant impacts on air quality and climate. However, current estimates of emissions from anthropogenic combustion are still subject to large uncertainties, especially in rapidly-developing regions. This hinders accurate assessments of their regional and global impacts on air quality and climate, which presents an urgent need to understand, assess, monitor, and predict anthropogenic combustion and associated emissions particularly at city-to-national scales. Combustion products co-emitted to the atmosphere and their relationships are typically related to characteristics of combustion processes. Thus, in order to understand anthropogenic combustion and associated emissions, my PhD study seeks to answer three major scientific questions: (1) To what extent could current observations of trace gases co-emitted from combustion be used to understand anthropogenic combustion, emissions, and related driving factors? (2) How well do present global climate-chemistry models simulate trace gases from combustion activities and could those models be used to study anthropogenic emissions? (3) To what extent could the current understanding of anthropogenic combustion and emissions be improved by jointly analyzing satellite, ground-based, aircraft measurements, and model simulations of trace gases co-emitted from combustion? To address the first scientific question, I combine air pollution measurements from multiple satellite instruments across 2005-2014 to characterize emergent features of the ratios of carbon monoxide (CO) and sulfate dioxide (SO2) to nitrogen dioxide (NO2) enhancements from anthropogenic emissions over 36 cities in China. The resulting emission pattern is well-correlated with economic development and traces a common emission pathway that resembles the evolution of air pollution in more developed cities. The absence of this progression in the current IPCC Representative Concentration Pathway emission inventory is most likely due to its deficient representation of the shift towards cleaner combustion in more developed cities. The results highlight the usefulness of augmenting observational capabilities by exploiting relationships of combustion tracers in constraining the temporal variation of emissions for gaseous pollutants. In addition, it is also desired to monitor and assess anthropogenic combustion and its impacts through modeling. Thus, to address the second scientific question, I evaluate simulations of two important anthropogenic combustion products (carbon dioxide (CO2) and CO) from a state-of-the-art high-resolution global prediction system, the Copernicus Atmosphere Monitoring Service (CAMS), by comparing with the Korea-United States Air Quality (KORUS-AQ) field measurements (May to June 2016) that aims to understand the factors controlling air quality over East Asia. The results show a slight overestimation for CAMS CO2 and a moderate underestimation for CAMS CO. CAMS also captures the observed more efficient combustion over Seoul compared to China outflows. Furthermore, to address both the second and third scientific questions, I combine observations and model simulations to uncover important combustion sources over East Asia, using the Community Atmosphere Model with chemistry (CAM-chem) with a CO tagging mechanism, where artificial CO tracers (i.e., tags) from specific sources are tracked as standard CO. With 17 CAM-chem tagged CO simulations using various model configurations, I quantify key regional sources of CO during KORUS-AQ. The results show that emissions from middle East Asia dominate continental outflows to Korea, while Korean emissions play an overall more important role for ground sites and plumes within the boundary layer in Korea. The CAM-chem tagging results are generally consistent with other source contribution approaches. Following the CO modeling, together with newly developed CO2 modeling and tagging mechanism in CAM-chem, I demonstrate the use of joint analysis of CO and CO2 towards a multi-species inversion. I simulate atmospheric CO2 as well as CO in CAM-chem using optimized carbon fluxes for CO2. The model results generally agree with observations from satellite, aircraft, and ground-based observations during KORUS-AQ. Then, I implement a CO2 tagging mechanism into the model. The modeled fossil fuel CO2 tags agree well with fossil fuel CO2 derived from radiocarbon samples during the field campaign. I also show that signatures of plume transport and sectoral emissions of CO2 are enhanced in CO analyses. Overall, this work elucidates the use of jointly analyzing CO2 and CO in tracking fossil fuel CO2, quantifying regional sources, and understanding combustion efficiency of sources. In my future work, I will (1) combine observations and model simulations of atmospheric gases to obtain improved estimates of their emissions from anthropogenic combustion based on inverse modeling techniques, and (2) use the improved emission estimates to quantify the impact of trace gases on air quality and climate