99 research outputs found

    Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series

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    Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.Sociedad Argentina de Informática e Investigación Operativa (SADIO

    A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

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    Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT

    Hybrid Models Based on Singular Values and Autoregressive Methods for Multistep Ahead Forecasting of Traffic Accidents

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    The traffic accidents occurrence urges the intervention of researchers and society; the human losses and material damage could be abated with scientific studies focused on supporting prevention plans. In this paper prediction strategies based on singular values and autoregressive models are evaluated for multistep ahead traffic accidents forecasting. Three time series of injured people in traffic accidents collected in Santiago de Chile from 2000:1 to 2014:12 were used, which were previously classified by causes related to the behavior of drivers, passengers, or pedestrians and causes not related to the behavior as road deficiencies, mechanical failures, and undetermined causes. A simplified form of Singular Spectrum Analysis (SSA), combined with the autoregressive linear (AR) method, and a conventional Artificial Neural Network (ANN) are proposed. Additionally, equivalent models that combine Hankel Singular Value Decomposition (HSVD), AR, and ANN are evaluated. The comparative analysis shows that the hybrid models SSA-AR and SSA-ANN reach the highest accuracy with an average MAPE of 1.5% and 1.9%, respectively, from 1- to 14-step ahead prediction. However, it was discovered that HSVD-AR shows a higher accuracy in the farthest horizons, from 12- to 14-step ahead prediction, which reaches an average MAPE of 2.2%

    Modeling travel demand and crashes at macroscopic and microscopic levels

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    Accurate travel demand / Annual Average Daily Traffic (AADT) and crash predictions helps planners to plan, propose and prioritize infrastructure projects for future improvements. Existing methods are based on demographic characteristics, socio-economic characteristics, and on-network (includes traffic volume) characteristics. A few methods have considered land use characteristics but along with other predictor variables. A strong correlation exists between land use characteristics and these other predictor variables. None of the past research has attempted to directly evaluate the effect and influence of land use characteristics on travel demand/AADT and crashes at both area and link level. These land use characteristics may be easy to capture and may have better predictive capabilities than other variables. The primary focus of this research is to develop macroscopic and microscopic models to estimate travel demand and crashes with an emphasis on land use characteristics. The proposed methodology involves development of macroscopic (area level) and microscopic (link level) models by incorporating scientific principles, statistical and artificial intelligent techniques. The microscopic models help evaluate the link level performance, whereas the macroscopic models help evaluate the overall performance of an area. The method for developing macroscopic models differs from microscopic models. The areas of land use characteristics were considered in developing macroscopic models, whereas the principle of demographic gravitation is incorporated in developing microscopic models. Statistical and back-propagation neural network (BPNN) techniques are used in developing the models. The results obtained indicate that statistical and neural network models ensured significantly lower errors. Overall, the BPNN models yielded better results in estimating travel demand and crashes than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT. Results obtained also indicate that land use characteristics have better predictive capabilities than other variables considered in this research. The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, prioritization of projects (short term and long term), and, to proactively apply safety treatments

    WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

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    The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

    Large-Scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility

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    Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the tra c management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to bene t the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools/algorithms/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the tra c prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using arti cial and real-world data are conducted. Each of the proposed approaches is compared with the state-of-the-art or currently existing approaches

    A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

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    Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately

    Integrating artificial neural networks, simulation and optimisation techniques in improving public emergency ambulance preparedness for heterogeneous regions under stochastic environments.

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    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The Bulawayo Emergency Medical Services (BEMS) department continues to rely on judgemental methods with limited use of historical data for future predictions, strategic, tactical and operational level decision making. The rural to urban migration trend has seen the sprouting of new residential areas, and this has put pressure to the limited health, housing and education resources. It is expected that as population increases, there is subsequent increase in demand for public emergency services. However, public emergency ambulance demand trends has been decreasing in Bulawayo over the years. This trend is a sign of limited capacity of the service rather than demand itself. The situation demanded for consolidated efforts across all sectors including research, to restore confidence among residents, reduce health risk and loss of lives. The key objective was to develop a framework that would assist in integrating forecasting, simulation and optimisation techniques for ambulance deployment to predefined locations with heterogeneous demand patterns under stochastic environments, using multiple performance indicators. Secondary data from the Bulawayo Municipality archives from 2010 to 2018 was used for model building and validation. A combination of methods based on mathematics, statistics, operations research and computer science were used for data analysis, model building, sensitivity analysis and numerical experiments. Results indicate that feed forward neural network (FFNN) models are superior to traditional SARIMA models in predicting ambulance demand, over a short-term forecasting horizon. The FFNN model is more inclined to value estimation as compared to SARIMA model, which is directional as depicted by the linear pattern over time. An ANN model with a 7-(4)-1 architecture was selected to forecast 2019 public emergency ambulance demand (PEAD). Peak PEAD is expected in January, March, September and December whilst lower demand is expected for April, June and July 2019. Simulation models developed mimicked the prevailing levels of service for BEMS with six(6) operational ambulances. However. the average response times were well above 15 minutes, with significantly high average queuing times and number of ambulances queuing for service. These performance outcomes were highly undesirable as they pose a great threat to human based outcomes of safety and satisfaction with regards to service delivery. Optimisation for simulation was conducted by simultaneously minimising the average response time and average queuing time, while maximising throughput ratios. Increasing the number of ambulances influenced the average response time below a certain threshold, beyond this threshold, the average response time remained constant rather than decreasing gradually. Ambulance utilisation inversely varied to increase in the feet size. Numerical experiments revealed that reducing the response time results in the reduction in number of ambulances required for optimal ambulance deployment. It is imperative to simultaneously consider multiple performance indicators in ambulance deployment as it balances resource allocation and capacity utilisation, while avoiding idleness of essential equipment and human resources. Management should lobby for de-congestion and resurfacing of old and dilapidated roads to increase access and speed when responding to emergency calls. Future research should investigate the influence of varying service time on optimum deployment plans and consider operational costs, wages and other budgetary constraints that influence the allocation of critical but scarce resources such as personnel, equipment and emergency ambulance response vehicles
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