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.^

Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

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.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

A comprehensive and integrative reconstruction of evolutionary history for Anomura (Crustacea: Decapoda)

Background The infraorder Anomura has long captivated the attention of evolutionary biologists due to its impressive morphological diversity and ecological adaptations. To date, 2500 extant species have been described but phylogenetic relationships at high taxonomic levels remain unresolved. Here, we reconstruct the evolutionary history—phylogeny, divergence times, character evolution and diversification—of this speciose clade. For this purpose, we sequenced two mitochondrial (16S and 12S) and three nuclear (H3, 18S and 28S) markers for 19 of the 20 extant families, using traditional Sanger and next-generation 454 sequencing methods. Molecular data were combined with 156 morphological characters in order to estimate the largest anomuran phylogeny to date. The anomuran fossil record allowed us to incorporate 31 fossils for divergence time analyses. Results Our best phylogenetic hypothesis (morphological + molecular data) supports most anomuran superfamilies and families as monophyletic. However, three families and eleven genera are recovered as para- and polyphyletic. Divergence time analysis dates the origin of Anomura to the Late Permian ~259 (224–296) MYA with many of the present day families radiating during the Jurassic and Early Cretaceous. Ancestral state reconstruction suggests that carcinization occurred independently 3 times within the group. The invasion of freshwater and terrestrial environments both occurred between the Late Cretaceous and Tertiary. Diversification analyses found the speciation rate to be low across Anomura, and we identify 2 major changes in the tempo of diversification; the most significant at the base of a clade that includes the squat-lobster family Chirostylidae. Conclusions Our findings are compared against current classifications and previous hypotheses of anomuran relationships. Many families and genera appear to be poly- or paraphyletic suggesting a need for further taxonomic revisions at these levels. A divergence time analysis provides key insights into the origins of major lineages and events and the timing of morphological (body form) and ecological (habitat) transitions. Living anomuran biodiversity is the product of 2 major changes in the tempo of diversification; our initial insights suggest that the acquisition of a crab-like form did not act as a key innovation.

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Improved estimation of postmortem interval with Cardiac Troponin T and improved analytical methods

Accurate knowledge of the time since death, or postmortem interval (PMI), has enormous legal, criminological, and psychological impact. In this study, an investigation was made to determine whether the relationship between the degradation of the human cardiac structure protein Cardiac Troponin T and PMI could be used as an indicator of time since death, thus providing a rapid, high resolution, sensitive, and automated methodology for the determination of PMI. ^ The use of Cardiac Troponin T (cTnT), a protein found in heart tissue, as a selective marker for cardiac muscle damage has shown great promise in the determination of PMI. An optimized conventional immunoassay method was developed to quantify intact and fragmented cTnT. A small sample of cardiac tissue, which is less affected than other tissues by external factors, was taken, homogenized, extracted with magnetic microparticles, separated by SDS-PAGE, and visualized with Western blot by probing with monoclonal antibody against cTnT. This step was followed by labeling and available scanners. This conventional immunoassay provides a proper detection and quantitation of cTnT protein in cardiac tissue as a complex matrix; however, this method does not provide the analyst with immediate results. Therefore, a competitive separation method using capillary electrophoresis with laser-induced fluorescence (CE-LIF) was developed to study the interaction between human cTnT protein and monoclonal anti-TroponinT antibody. ^ Analysis of the results revealed a linear relationship between the percent of degraded cTnT and the log of the PMI, indicating that intact cTnT could be detected in human heart tissue up to 10 days postmortem at room temperature and beyond two weeks at 4C. The data presented demonstrates that this technique can provide an extended time range during which PMI can be more accurately estimated as compared to currently used methods. The data demonstrates that this technique represents a major advance in time of death determination through a fast and reliable, semi-quantitative measurement of a biochemical marker from an organ protected from outside factors. ^

Estimation of ravine sediment production and sediment dynamics in the lower Le Sueur River watershed, Minnesota

This study focuses on quantifying explicitly the sediment budget of deeply incised ravines in the lower Le Sueur River watershed, in southern Minnesota. High-rate-gully-erosion equations along with the Universal Soil Loss Equation (USLE) were implemented in a numerical modeling approach that is based on a time-integration of the sediment balance equations. The model estimates the rates of ravine width and depth change and the amount of sediment periodically flushing from the ravines. Components of the sediment budget of the ravines were simulated with the model and results suggest that the ravine walls are the major sediment source in the ravines. A sensitivity analysis revealed that the erodibility coefficients of the gully bed and wall, the local slope angle and the Manning’s coefficient are the key parameters controlling the rate of sediment production. Recommendations to guide further monitoring efforts in the watershed and increased detail modeling approaches are highlighted as a result of this modeling effort.

An improved framework for dynamic origin-destination (O-D) matrix estimation

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou's model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou's model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

Methodology to create cost estimation models for engineer-to-order operations

This research addresses the problem of cost estimation for product development in engineer-to-order (ETO) operations. An ETO operation starts the product development process with a product specification and ends with delivery of a rather complicated, highly customized product. ETO operations are practiced in various industries such as engineering tooling, factory plants, industrial boilers, pressure vessels, shipbuilding, bridges and buildings. ETO views each product as a delivery item in an industrial project and needs to make an accurate estimation of its development cost at the bidding and/or planning stage before any design or manufacturing activity starts. ^ Many ETO practitioners rely on an ad hoc approach to cost estimation, with use of past projects as reference, adapting them to the new requirements. This process is often carried out on a case-by-case basis and in a non-procedural fashion, thus limiting its applicability to other industry domains and transferability to other estimators. In addition to being time consuming, this approach usually does not lead to an accurate cost estimate, which varies from 30% to 50%. ^ This research proposes a generic cost modeling methodology for application in ETO operations across various industry domains. Using the proposed methodology, a cost estimator will be able to develop a cost estimation model for use in a chosen ETO industry in a more expeditious, systematic and accurate manner. ^ The development of the proposed methodology was carried out by following the meta-methodology as outlined by Thomann. Deploying the methodology, cost estimation models were created in two industry domains (building construction and the steel milling equipment manufacturing). The models are then applied to real cases; the cost estimates are significantly more accurate than the actual estimates, with mean absolute error rate of 17.3%. ^ This research fills an important need of quick and accurate cost estimation across various ETO industries. It differs from existing approaches to the problem in that a methodology is developed for use to quickly customize a cost estimation model for a chosen application domain. In addition to more accurate estimation, the major contributions are in its transferability to other users and applicability to different ETO operations. ^

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Reducing uncertainties in estimation of wind effects on tall buildings using aerodynamic wind tunnel tests

Tall buildings are wind-sensitive structures and could experience high wind-induced effects. Aerodynamic boundary layer wind tunnel testing has been the most commonly used method for estimating wind effects on tall buildings. Design wind effects on tall buildings are estimated through analytical processing of the data obtained from aerodynamic wind tunnel tests. Even though it is widely agreed that the data obtained from wind tunnel testing is fairly reliable the post-test analytical procedures are still argued to have remarkable uncertainties. This research work attempted to assess the uncertainties occurring at different stages of the post-test analytical procedures in detail and suggest improved techniques for reducing the uncertainties. Results of the study showed that traditionally used simplifying approximations, particularly in the frequency domain approach, could cause significant uncertainties in estimating aerodynamic wind-induced responses. Based on identified shortcomings, a more accurate dual aerodynamic data analysis framework which works in the frequency and time domains was developed. The comprehensive analysis framework allows estimating modal, resultant and peak values of various wind-induced responses of a tall building more accurately. Estimating design wind effects on tall buildings also requires synthesizing the wind tunnel data with local climatological data of the study site. A novel copula based approach was developed for accurately synthesizing aerodynamic and climatological data up on investigating the causes of significant uncertainties in currently used synthesizing techniques. Improvement of the new approach over the existing techniques was also illustrated with a case study on a 50 story building. At last, a practical dynamic optimization approach was suggested for tuning structural properties of tall buildings towards attaining optimum performance against wind loads with less number of design iterations.

Real-time scheduling of embedded applications on multi-core platforms

For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level. We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.