Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Healthy Eating Index and Alternate Healthy Eating Index among Haitian Americans and African Americans with and without Type 2 Diabetes

Ethnicities within Black populations have not been distinguished in most nutrition studies. We sought to examine dietary differences between African Americans (AA) and Haitian Americans (HA) with and without type 2 diabetes using the Healthy Eating Index, 2005 (HEI-05), and the Alternate Healthy Eating Index (AHEI). The design was cross-sectional (225 AA, 246 HA) and recruitment was by community outreach. The eating indices were calculated from data collected with the Harvard food-frequency questionnaire. African Americans had lower HEI-05 scores (−8.67, 13.1); , than HA. Haitian American females and AA males had higher AHEI than AA females and HA males, respectively, () adjusting for age and education. Participants with diabetes had higher adherence to the HEI-05 (1.78, 6.01), , and lower adherence to the AHEI (16.3, −3.19), , , than participants without diabetes. The findings underscore the importance of disaggregating ethnicities and disease state when assessing diet.

Combination of 16S rRNA variable regions provides a detailed analysis of bacterial community dynamics in the lungs of cystic fibrosis patients

Chronic bronchopulmonary bacterial infections remain the most common cause of morbidity and mortality among patients with cystic fibrosis (CF). Recent community sequencing work has now shown that the bacterial community in the CF lung is polymicrobial. Identifying bacteria in the CF lung through sequencing can be costly and is not practical for many laboratories. Molecular techniques such as terminal restriction fragment length polymorphism or amplicon length heterogeneity-polymerase chain reaction (LH-PCR) can provide many laboratories with the ability to study CF bacterial communities without costly sequencing. The aim of this study was to determine if the use of LH-PCR with multiple hypervariable regions of the 16S rRNA gene could be used to identify organisms found in sputum DNA. This work also determined if LH-PCR could be used to observe the dynamics of lung infections over a period of time. Nineteen samples were analysed with the V1 and the V1_V2 region of the 16S rRNA gene. Based on the amplicon size present in the V1_V2 region, Pseudomonas aeruginosa was confirmed to be in all 19 samples obtained from the patients. The V1 region provided a higher power of discrimination between bacterial profiles of patients. Both regions were able to identify trends in the bacterial population over a period of time. LH profiles showed that the CF lung community is dynamic and that changes in the community may in part be driven by the patient's antibiotic treatment. LH-PCR is a tool that is well suited for studying bacterial communities and their dynamics.

Ocean Acidification Outweighs Nutrient Effects in Structuring Seagrass Epiphyte Communities

Developing a framework for assessing interactions between multiple anthropogenic stressors remains an important goal in environmental research. In coastal ecosystems, the relative effects of aspects of global climate change (e.g. CO2 concentrations) and localized stressors (e.g. eutrophication), in combination, have received limited attention. Using a long-term (11 month) field experiment, we examine how epiphyte assemblages in a tropical seagrass meadow respond to factorial manipulations of dissolved carbon dioxide (CO2(aq)) and nutrient enrichment. In situ CO2(aq) manipulations were conducted using clear, open-top chambers, which replicated carbonate parameter forecasts for the year 2100. Nutrient enrichment consisted of monthly additions of slow-release fertilizer, nitrogen (N) and phosphorus (P), to the sediments at rates equivalent to theoretical maximum rates of anthropogenic loading within the region (1.54 g N m−2 d−1 and 0.24 g P m−2 d−1). Epiphyte community structure was assessed on a seasonal basis and revealed declines in the abundance of coralline algae, along with increases in filamentous algae under elevated CO2(aq). Surprisingly, nutrient enrichment had no effect on epiphyte community structure or overall epiphyte loading. Interactions between CO2(aq) and nutrient enrichment were not detected. Furthermore, CO2(aq)-mediated responses in the epiphyte community displayed strong seasonality, suggesting that climate change studies in variable environments should be conducted over extended time-scales. Synthesis. The observed responses indicate that for certain locations, global stressors such as ocean acidification may take precedence over local eutrophication in altering the community structure of seagrass epiphyte assemblages. Given that nutrient-driven algal overgrowth is commonly cited as a widespread cause of seagrass decline, our findings highlight that alternate climate change forces may exert proximate control over epiphyte community structure.

Exodus: Alternate Documents [Press Release]

This is the press release for "Exodus: Alternate Documents" held from September 13th to October 31st, 2014.

Screening of pharmaceuticals and other pollutants of concern by a combination of chemical and biological methods

This study examined the occurrence of pharmaceuticals and personal care products (PPCP's) in surface waters of Florida and their potential to be use as indicators of wastewater contamination. Previous studies have shown that elimination of pharmaceuticals in municipal sewage treatment plants is often incomplete. Aquatic ecosystems are under increased stress from human activities, particularly in heavily populated areas. The purpose of this study was to find an ideal indicator for wastewater. The applied methods, GC/MS and LC/MS, were suitable for the determination of pharmaceuticals and personal care products in aqueous environmental samples to the lower parts-per-trillion (ng/L) level. As a result of this study a snapshot view of the occurrence of pharmaceuticals and personal care products in south Florida was produced. PPCP's were commonly detected in coastal environments of South Florida at relatively low concentrations. In general, PPCP's were higher inside the canals and contained bodies of water than in open water systems. Caffeine was successfully used to describe impacted versus pristine locations. However, no particular correlation was observed among caffeine and other traditional water quality parameters.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.