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.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR)

Interferometric synthetic aperture radar (InSAR) techniques can successfully detect phase variations related to the water level changes in wetlands and produce spatially detailed high-resolution maps of water level changes. Despite the vast details, the usefulness of the wetland InSAR observations is rather limited, because hydrologists and water resources managers need information on absolute water level values and not on relative water level changes. We present an InSAR technique called Small Temporal Baseline Subset (STBAS) for monitoring absolute water level time series using radar interferograms acquired successively over wetlands. The method uses stage (water level) observation for calibrating the relative InSAR observations and tying them to the stage's vertical datum. We tested the STBAS technique with two-year long Radarsat-1 data acquired during 2006–2008 over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). The InSAR-derived water level data were calibrated using 13 stage stations located in the study area to generate 28 successive high spatial resolution maps (50 m pixel resolution) of absolute water levels. We evaluate the quality of the STBAS technique using a root mean square error (RMSE) criterion of the difference between InSAR observations and stage measurements. The average RMSE is 6.6 cm, which provides an uncertainty estimation of the STBAS technique to monitor absolute water levels. About half of the uncertainties are attributed to the accuracy of the InSAR technique to detect relative water levels. The other half reflects uncertainties derived from tying the relative levels to the stage stations' datum.

Correspondence of historic salinity fluctuations in Florida Bay, USA, to atmospheric variability and anthropogenic changes

Florida Bay is a highly dynamic estuary that exhibits wide natural fluctuations in salinity due to changes in the balance of precipitation, evaporation and freshwater runoff from the mainland. Rapid and large-scale modification of freshwater flow and construction of transportation conduits throughout the Florida Keys during the late nineteenth and twentieth centuries reshaped water circulation and salinity patterns across the ecosystem. In order to determine long-term patterns in salinity variation across the Florida Bay estuary, we used a diatom-based salinity transfer function to infer salinity within 3.27 ppt root mean square error of prediction from diatom assemblages from four ~130 year old sediment records. Sites were distributed along a gradient of exposure to anthropogenic shifts in the watershed and salinity. Precipitation was found to be the primary driver influencing salinity fluctuations over the entire record, but watershed modifications on the mainland and in the Florida Keys during the late-1800s and 1900s were the most likely cause of significant shifts in baseline salinity. The timing of these shifts in the salinity baseline varies across the Bay: that of the northeastern coring location coincides with the construction of the Florida Overseas Railway (AD 1906–1916), while that of the east-central coring location coincides with the drainage of Lake Okeechobee (AD 1881–1894). Subsequent decreases occurring after the 1960s (east-central region) and early 1980s (southwestern region) correspond to increases in freshwater delivered through water control structures in the 1950s–1970s and again in the 1980s. Concomitant increases in salinity in the northeastern and south-central regions of the Bay in the mid-1960s correspond to an extensive drought period and the occurrence of three major hurricanes, while the drop in the early 1970s could not be related to any natural event. This paper provides information about major factors influencing salinity conditions in Florida Bay in the past and quantitative estimates of the pre- and post-South Florida watershed modification salinity levels in different regions of the Bay. This information should be useful for environmental managers in setting restoration goals for the marine ecosystems in South Florida, especially for Florida Bay.

Statistical Models for Predicting College Success

Colleges base their admission decisions on a number of factors to determine which applicants have the potential to succeed. This study utilized data for students that graduated from Florida International University between 2006 and 2012. Two models were developed (one using SAT as the principal explanatory variable and the other using ACT as the principal explanatory variable) to predict college success, measured using the student’s college grade point average at graduation. Some of the other factors that were used to make these predictions were high school performance, socioeconomic status, major, gender, and ethnicity. The model using ACT had a higher R^2 but the model using SAT had a lower mean square error. African Americans had a significantly lower college grade point average than graduates of other ethnicities. Females had a significantly higher college grade point average than males.

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.

Compilation of planktonic foraminiferal census counts from the Southern Hemisphere Oceans

We present an improved database of planktonic foraminiferal census counts from the Southern Hemisphere Oceans (SHO) from 15°S to 64°S. The SHO database combines 3 existing databases. Using this SHO database, we investigated dissolution biases that might affect faunal census counts. We suggest a depth/[DCO3]2- threshold of ~3800 m/[DCO3]2- = ~-10 to -5 µmol/kg for the Pacific and Indian Oceans, and ~4000 m/[DCO3]2- = ~0 to 10 µmol/kg for the Atlantic Ocean, under which core-top assemblages can be affected by dissolution and are less reliable for paleo-sea surface temperature (SST) reconstructions. We removed all core-tops beyond these thresholds from the SHO database. This database has 598 core-tops and is able to reconstruct past SST variations from 2° to 25.5°C, with a root mean square error of 1.00°C, for annual temperatures. To inspect dissolution affects SST reconstruction quality, we tested the data base with two "leave-one-out" tests, with and without the deep core-tops. We used this database to reconstruct Summer SST (SSST) over the last 20 ka, using the Modern Analog Technique method, on the Southeast Pacific core MD07-3100. This was compared to the SSST reconstructed using the 3 databases used to compile the SHO database. Thus showing that the reconstruction using the SHO database is more reliable, as its dissimilarity values are the lowest. The most important aspect here is the importance of a bias-free, geographic-rich, database. We leave this dataset open-ended to future additions; the new core-tops must be carefully selected, with their chronological frameworks, and evidence of dissolution assessed.

Determinação de teor de biodieseis de algodão e pinhão manso em misturas com diesel usando espectroscopia no infravermelho médio e quadrados mínimos parciais

Biodiesel is a renewable fuel derived from vegetable oils or animal fats, which can be a total or partial substitute for diesel. Since 2005, this fuel was introduced in the Brazilian energy matrix through Law 11.097 that determines the percentage of biodiesel added to diesel oil as well as monitoring the insertion of this fuel in market. The National Agency of Petroleum, Natural Gas and Biofuels (ANP) establish the obligation of adding 7% (v/v) of biodiesel to diesel commercialized in the country, making crucial the analytical control of this content. Therefore, in this study were developed and validated methodologies based on the use of Mid Infrared Spectroscopy (MIR) and Multivariate Calibration by Partial Least Squares (PLS) to quantify the methyl and ethyl biodiesels content of cotton and jatropha in binary blends with diesel at concentration range from 1.00 to 30.00% (v/v), since this is the range specified in standard ABNT NBR 15568. The biodiesels were produced from two routes, using ethanol or methanol, and evaluated according to the parameters: oxidative stability, water content, kinematic viscosity and density, presenting results according to ANP Resolution No. 45/2014. The built PLS models were validated on the basis of ASTM E1655-05 for Infrared Spectroscopy and Multivariate Calibration and ABNT NBR 15568, with satisfactory results due to RMSEP (Root Mean Square Error of Prediction) values below 0.08% (<0.1%), correlation coefficients (R) above 0.9997 and the absence of systematic error (bias). Therefore, the methodologies developed can be a promising alternative in the quality control of this fuel.

Numerical Simulations for the Design of Novel Integrated Parallel Reception, Excitation, and Shimming (iPRES) Coil Arrays

Magnetic field inhomogeneity results in image artifacts including signal loss, image blurring and distortions, leading to decreased diagnostic accuracy. Conventional multi-coil (MC) shimming method employs both RF coils and shimming coils, whose mutual interference induces a tradeoff between RF signal-to-noise (SNR) ratio and shimming performance. To address this issue, RF coils were integrated with direct-current (DC) shim coils to shim field inhomogeneity while concurrently emitting and receiving RF signal without being blocked by the shim coils. The currents applied to the new coils, termed iPRES (integrated parallel reception, excitation and shimming), were optimized in the numerical simulation to improve the shimming performance. The objectives of this work is to offer a guideline for designing the optimal iPRES coil arrays to shim the abdomen.

In this thesis work, the main field () inhomogeneity was evaluated by root mean square error (RMSE). To investigate the shimming abilities of iPRES coil arrays, a set of the human abdomen MRI data was collected for the numerical simulations. Thereafter, different simplified iPRES(N) coil arrays were numerically modeled, including a 1-channel iPRES coil and 8-channel iPRES coil arrays. For 8-channel iPRES coil arrays, each RF coil was split into smaller DC loops in the x, y and z direction to provide extra shimming freedom. Additionally, the number of DC loops in a RF coil was increased from 1 to 5 to find the optimal divisions in z direction. Furthermore, switches were numerically implemented into iPRES coils to reduce the number of power supplies while still providing similar shimming performance with equivalent iPRES coil arrays.

The optimizations demonstrate that the shimming ability of an iPRES coil array increases with number of DC loops per RF coil. Furthermore, the z direction divisions tend to be more effective in reducing field inhomogeneity than the x and y divisions. Moreover, the shimming performance of an iPRES coil array gradually reach to a saturation level when the number of DC loops per RF coil is large enough. Finally, when switches were numerically implemented in the iPRES(4) coil array, the number of power supplies can be reduced from 32 to 8 while keeping the shimming performance similar to iPRES(3) and better than iPRES(1). This thesis work offers a guidance for the designs of iPRES coil arrays.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

FTIR-inferred biogeochemical properties for the past 3.6 Myr in the sediment record of core ICDP5011-1 in Lake Elgygytgyn, NE Russia

A number of studies have shown that Fourier transform infrared spectroscopy (FTIR) can be applied to quantitatively assess lacustrine sediment constituents. In this study, we developed calibration models based on FTIR for the quantitative determination of biogenic silica (BSi; n = 420; gradient: 0.9-56.5%), total organic carbon (TOC; n = 309; gradient: 0-2.9%), and total inorganic carbon (TIC; n= 152; gradient: 0-0.4%) in a 318 m-long sediment record with a basal age of 3.6 million years from Lake El'gygytgyn, Far East Russian Arctic. The developed partial least squares (PLS) regression models yield high cross-validated (CV) R2CV = 0.86-0.91 and low root mean square error of cross-validation (RMSECV) (3.1-7.0% of the gradient for the different properties). By applying these models to 6771 samples from the entire sediment record, we obtained detailed insight into bioproductivity variations in Lake El'gygytgyn throughout the middle to late Pliocene and Quaternary. High accumulation rates of BSi indicate a productivity maximum during the middle Pliocene (3.6-3.3 Ma), followed by gradually decreasing rates during the late Pliocene and Quaternary. The average BSi accumulation during the middle Pliocene was ~3 times higher than maximum accumulation rates during the past 1.5 million years. The indicated progressive deterioration of environmental and climatic conditions in the Siberian Arctic starting at ca. 3.3 Ma is consistent with the first occurrence of glacial periods and the finally complete establishment of glacial-interglacial cycles during the Quaternary.

Reconstructions of eolian dust accumulation in northwest African margin sediments

Reconstructions of eolian dust accumulation in northwest African margin sediments provide important continuous records of past changes in atmospheric circulation and aridity in the region. Existing records indicate dramatic changes in North African dust emissions over the last 20 ka, but the limited spatial extent of these records and the lack of high-resolution flux data do not allow us to determine whether changes in dust deposition occurred with similar timing, magnitude and abruptness throughout northwest Africa. Here we present new records from a meridional transect of cores stretching from 31°N to 19°N along the northwest African margin. By combining grain size endmember modeling with 230Th-normalized fluxes for the first time, we are able to document spatial and temporal changes in dust deposition under the North African dust plume throughout the last 20 ka. Our results provide quantitative estimates of the magnitude of dust flux changes associated with Heinrich Stadial 1, the Younger Dryas, and the African Humid Period (AHP; ~11.7-5 ka), offering robust targets for model-based estimates of the climatic and biogeochemical impacts of past changes in North African dust emissions. Our data suggest that dust fluxes between 8 and 6 ka were a factor of ~5 lower than average fluxes during the last 2 ka. Using a simple model to estimate the effects of bioturbation on dust input signals, we find that our data are consistent with abrupt, synchronous changes in dust fluxes in all cores at the beginning and end of the AHP. The mean ages of these transitions are 11.8±0.2 ka (1Sigma) and 4.9±0.2 ka, respectively.

Planktic foraminifera abundances and winter SST reconstruction for sediment core SO90-39KG

The Indian winter monsoon (IWM) is a key component of the seasonally changing monsoon system that affects the densely populated regions of South Asia. Cold winds originating in high northern latitudes provide a link of continental-scale Northern Hemisphere climate to the tropics. Western Disturbances (WD) associated with the IWM play a critical role for the climate and hydrology in northern India and the western Himalaya region. It is vital to understand the mechanisms and teleconnections that influence IWM variability to better predict changes in future climate. Here we present a study of regionally calibrated winter (January) temperatures and according IWM intensities, based on a planktic foraminiferal record with biennial (2.55 years) resolution. Over the last ~250 years, IWM intensities gradually weakened, based on the long-term trend of reconstructed January temperatures. Furthermore, the results indicate that IWM is connected on interannual- to decadal time scales to climate variability of the tropical and extratropical Pacific, via El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). However, our findings suggest that this relationship appeared to begin to decouple since the beginning of the 20th century. Cross-spectral analysis revealed that several distinct decadal-scale phases of colder climate and accordingly more intense winter monsoon centered at the years ~1800, ~1890 and ~1930 can be linked to changes of the North Atlantic Oscillation (NAO).