Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Model reduction for nonlinear aeroelasticity using the Discrete Empirical Interpolation Method

A novel surrogate model is proposed in lieu of computational fluid dynamic (CFD) code for fast nonlinear aerodynamic modeling. First, a nonlinear function is identified on selected interpolation points defined by discrete empirical interpolation method (DEIM). The flow field is then reconstructed by a least square approximation of flow modes extracted by proper orthogonal decomposition (POD). The proposed model is applied in the prediction of limit cycle oscillation for a plunge/pitch airfoil and a delta wing with linear structural model, results are validate against a time accurate CFD-FEM code. The results show the model is able to replicate the aerodynamic forces and flow fields with sufficient accuracy while requiring a fraction of CFD cost.

Risk Prediction In Cardiovascular Epidemiology: Considerations for Modeling Composite Endpoints

Thesis (Master's)--University of Washington, 2016-08

Designing for User-facing Uncertainty in Everyday Sensing and Prediction

Thesis (Ph.D.)--University of Washington, 2016-07

Wind wave prediction in the Caspian Sea

In this thesis, wind wave prediction and analysis in the Southern Caspian Sea are surveyed. Because of very much importance and application of this matter in reducing vital and financial damages or marine activities, such as monitoring marine pollution, designing marine structure, shipping, fishing, offshore industry, tourism and etc, gave attention by some marine activities. In this study are used the Caspian Sea topography data that are extracted from the Caspian Sea Hydrography map of Iran Armed Forces Geographical Organization and the I 0 meter wind field data that are extracted from the transmitted GTS synoptic data of regional centers to Forecasting Center of Iran Meteorological Organization for wave prediction and is used the 20012 wave are recorded by the oil company's buoy that was located at distance 28 Kilometers from Neka shore for wave analysis. The results of this research are as follows: - Because of disagreement between the prediction results of SMB method in the Caspian sea and wave data of the Anzali and Neka buoys. The SMB method isn't able to Predict wave characteristics in the Southern Caspian Sea. - Because of good relativity agreement between the WAM model output in the Caspian Sea and wave data of the Anzali buoy. The WAM model is able to predict wave characteristics in the southern Caspian Sea with high relativity accuracy. The extreme wave height distribution function for fitting to the Southern Caspian Sea wave data is obtained by determining free parameters of Poisson-Gumbel function through moment method. These parameters are as below: A=2.41, B=0.33. The maximum relative error between the estimated 4-year return value of the Southern Caspian Sea significant wave height by above function with the wave data of Neka buoy is about %35. The 100-year return value of the Southern Caspian Sea significant height wave is about 4.97 meter. The maximum relative error between the estimated 4-year return value of the Southern Caspian Sea significant wave height by statistical model of peak over threshold with the wave data of Neka buoy is about %2.28. The parametric relation for fitting to the Southern Caspian Sea frequency spectra is obtained by determining free parameters of the Strekalov, Massel and Krylov etal_ multipeak spectra through mathematical method. These parameters are as below: A = 2.9 B=26.26, C=0.0016 m=0.19 and n=3.69. The maximum relative error between calculated free parameters of the Southern Caspian Sea multipeak spectrum with the proposed free parameters of double-peaked spectrum by Massel and Strekalov on the experimental data from the Caspian Sea is about 36.1 % in spectrum energetic part and is about 74M% in spectrum high frequency part. The peak over threshold waverose of the Southern Caspian Sea shows that maximum occurrence probability of wave height is relevant to waves with 2-2.5 meters wave fhe error sources in the statistical analysis are mainly due to: l) the missing wave data in 2 years duration through battery discharge of Neka buoy. 2) the deportation %15 of significant height annual mean in single year than long period average value that is caused by lack of adequate measurement on oceanic waves, and the error sources in the spectral analysis are mainly due to above- mentioned items and low accurate of the proposed free parameters of double-peaked spectrum on the experimental data from the Caspian Sea.

Estimating glomerular filtration rate in acute coronary syndromes: Different equations, different mortality risk prediction

AIMS: Renal dysfunction is a powerful predictor of adverse outcomes in patients hospitalized for acute coronary syndrome. Three new glomerular filtration rate (GFR) estimating equations recently emerged, based on serum creatinine (CKD-EPIcreat), serum cystatin C (CKD-EPIcyst) or a combination of both (CKD-EPIcreat/cyst), and they are currently recommended to confirm the presence of renal dysfunction. Our aim was to analyse the predictive value of these new estimated GFR (eGFR) equations regarding mid-term mortality in patients with acute coronary syndrome, and compare them with the traditional Modification of Diet in Renal Disease (MDRD-4) formula. METHODS AND RESULTS: 801 patients admitted for acute coronary syndrome (age 67.3±13.3 years, 68.5% male) and followed for 23.6±9.8 months were included. For each equation, patient risk stratification was performed based on eGFR values: high-risk group (eGFR<60ml/min per 1.73m2) and low-risk group (eGFR⩾60ml/min per 1.73m2). The predictive performances of these equations were compared using area under each receiver operating characteristic curves (AUCs). Overall risk stratification improvement was assessed by the net reclassification improvement index. The incidence of the primary endpoint was 18.1%. The CKD-EPIcyst equation had the highest overall discriminate performance regarding mid-term mortality (AUC 0.782±0.20) and outperformed all other equations (ρ<0.001 in all comparisons). When compared with the MDRD-4 formula, the CKD-EPIcyst equation accurately reclassified a significant percentage of patients into more appropriate risk categories (net reclassification improvement index of 11.9% (p=0.003)). The CKD-EPIcyst equation added prognostic power to the Global Registry of Acute Coronary Events (GRACE) score in the prediction of mid-term mortality. CONCLUSION: The CKD-EPIcyst equation provides a novel and improved method for assessing the mid-term mortality risk in patients admitted for acute coronary syndrome, outperforming the most widely used formula (MDRD-4), and improving the predictive value of the GRACE score. These results reinforce the added value of cystatin C as a risk marker in these patients.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

PCA-FNN based performance prediction for water injection in oilfields.

In order to solve the problem of uncertain cycle of water injection in the oilfield, this paper proposed a numerical method based on PCA-FNN, so that it can forecast the effective cycle of water injection. PCA is used to reduce the dimension of original data, while FNN is applied to train and test the new data. The correctness of PCA-FNN model is verified by the real injection statistics data from 116 wells of an oilfield, the result shows that the average absolute error and relative error of the test are 1.97 months and 10.75% respectively. The testing accuracy has been greatly improved by PCA-FNN model compare with the FNN which has not been processed by PCA and multiple liner regression method. Therefore, PCA-FNN method is reliable to forecast the effectiveness cycle of water injection and it can be used as an decision-making reference method for the engineers.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

COMPUTATIONAL PREDICTION OF THE SPORULATION NETWORK IN CLOSTRIDIUM THERMOCELLUM

Sporulation is a process in which some bacteria divide asymmetrically to form tough protective endospores, which help them to survive in a hazardous environment for a quite long time. The factors which can trigger this process are diverse. Heat, radiation, chemicals and lacking of nutrition can all lead to the formation of endospores. This phenomenon will lead to low productivity during industrial production. However, the sporulation mechanism in a spore-forming bacterium, Clostridium theromcellum, is still unclear. Therefore, if a regulation network of sporulation can be built, we may figure out ways to inhibit this process. In this study, a computational method is applied to predict the sporulation network in Clostridium theromcellum. A working sporulation network model with 40 new predicted genes and 4 function groups is built by using a network construction program, CINPER. 5 sets of microarray expression data in Clostridium theromcellum under different conditions have been collected. The analysis shows the predicted result is reasonable.

The Performance and Service Life Prediction of High Performance Concrete in Sulfate and Acidic Environments

Concrete substructures are often subjected to environmental deterioration, such as sulfate and acid attack, which leads to severe damage and causes structure degradation or even failure. In order to improve the durability of concrete, the High Performance Concrete (HPC) has become widely used by partially replacing cement with pozzolanic materials. However, HPC degradation mechanisms in sulfate and acidic environments are not completely understood. It is therefore important to evaluate the performance of the HPC in such conditions and predict concrete service life by establishing degradation models. This study began with a review of available environmental data in the State of Florida. A total of seven bridges have been inspected. Concrete cores were taken from these bridge piles and were subjected for microstructural analysis using Scanning Electron Microscope (SEM). Ettringite is found to be the products of sulfate attack in sulfate and acidic condition. In order to quantitatively analyze concrete deterioration level, an image processing program is designed using Matlab to obtain quantitative data. Crack percentage (Acrack/Asurface) is used to evaluate concrete deterioration. Thereafter, correlation analysis was performed to find the correlation between five related variables and concrete deterioration. Environmental sulfate concentration and bridge age were found to be positively correlated, while environmental pH level was found to be negatively correlated. Besides environmental conditions, concrete property factor was also included in the equation. It was derived from laboratory testing data. Experimental tests were carried out implementing accelerated expansion test under controlled environment. Specimens of eight different mix designs were prepared. The effect of pozzolanic replacement rate was taken into consideration in the empirical equation. And the empirical equation was validated with existing bridges. Results show that the proposed equations compared well with field test results with a maximum deviation of ± 20%. Two examples showing how to use the proposed equations are provided to guide the practical implementation. In conclusion, the proposed approach of relating microcracks to deterioration is a better method than existing diffusion and sorption models since sulfate attack cause cracking in concrete. Imaging technique provided in this study can also be used to quantitatively analyze concrete samples.