A Machine Learning Approach to Traffic Congestion Hotspot Identification and Prediction

The cost of accidents is devastating to the U.S. economy. The National Highway Traffic Safety Administration (NHTSA) estimates that over $230 billion is incurred annually through lost workplace productivity, damage, medical costs, travel delays, lost household productivity, insurance costs, legal costs, workplace costs, and emergency services. In 2005 approximately 6,420,000 car accidents occurred in the United States, injuring 2.9 million people and killing 42,636 persons. (Car Accident Statistics, 2010; Wenske, 2002).Although traffic fatalities have decreased almost 20% in the last 30 years, traffic fatalities have increased in the last few years. NHTSA (2010a) estimates that up to one-third of recent crashes and up to two-thirds of the ensuing fatalities are from driving, or behavior.Aggressive Driving/Road RageDefinitionsThe terms, aggressive and road rage are often used synonymously but there are differences according to traffic authorities. NHTSA defines Aggressive Driving . . .as occurring when an individual commits combination of moving traffic offenses so as to endanger other persons or property (NHTSA, 20106, p. 1). While driving is traffic offense, is categorized as criminal offense. Road Rage is defined as. . .an assault with motor vehicle or other dangerous weapon by the operator or passenger(s) of another motor vehicle or an assault precipitated by an incident that occurred on roadway (NHTSA, 2010c, p. 1).BehaviorsAggressive drivers often display the following behaviors: ...exceeding the posted speed limit, following too closely, erratic or unsafe lane changes, improperly signaling lane changes, [and the] failure to obey traffic control devices (stop signs, yield signs, traffic signals, [and] railroad grade cross signals (p. 1). Running red light is one of the most dangerous types. The Insurance Institute for Highway Safety estimates that 250,000 crashes year are attributable to running red lights (Fumento, 1998). In an NHTSA survey, over 60% of respondents felt personally threatened for themselves and their families due to unsafe drivers (NHTSA, 2010d).Contributing CausesRoad congestion. Increased traffic causes increased potential for rage. Newly constructed miles in the United States have increased 1 % since 1 987, but the number of miles driven has increased by 35%. In addition, close to 70% of urban areas have highly congested freeways at rush hour as compared to 20 years ago (Vest, Cohen, & Tharp, 1 997). The increase in daily driving added to life stressors can result in build-up of negative mood and stress. This can create an atmosphere amenable to driving (Calovski & Blanchard, 2004).Characteristics of driving. In one study of 298 drivers aged 17 to 86, a measure of driving predicted more frequent and more error-prone overtaking, which are effects attributed to the use of confrontive coping strategies in interaction with other vehicles (Matthews et al., 1 998, p. 1 36). Drivers are at greater risk of accidents if they are unaware that emotions are major contributing factor in crashes. Drivers are more likely to be in crashes if they are not willing or able to call up emotional coping strategies in the face of driver stress (Legree, Heffner, Psotka, Martin, & Medsker, 2003).Cell-phone use triggers driver aggression. Cell phone use is cognitively distracting enough to . . .adversely [affect] driver's ability to appropriately react to . . .roadway conditions requiring change in speed or direction. . .to brake when following another driver. . .[or an] ability to brake in response to red light... (McGarva, Ramsey, & Shear, 2006, p. 134). Drivers using cell phones may also react slower when red light turns to green. This can anger other drivers behind the cell phone user's vehicle. …

It is inspiring to notice that legislation that helps incorporate autonomous vehicles on our roads in the near future is moving forward. The National Highway Traffic Safety Administration (NHTSA), which is part of the U.S. Department of Transportation (USDoT), has integrated automated vehicles into the existing safety standards such that the text in the standards do not rely on the existence of steering wheels and driver’s seats, which may not exist in fully autonomous vehicles. In addition, the NHTSA has incorporated lane-keeping support, pedestrian automatic emergency braking, blind spot detection, and blind spot intervention into its Five-Star Safety Ratings program. Such driver-assistance technologies are the first steps toward fully autonomous vehicles but, more importantly, toward safer ones. Developing safer vehicles requires significant investments, so it is important that the legislation provide a framework that is predictable, reducing the risk of long-term commitment.

Aim This study aims to enhance safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). By acquiring and preprocessing various datasets, implementing feature engineering, and using algorithms like SVM, decision trees, ANN, and random forests, the study demonstrates the effectiveness of ML models in risk prediction and mitigation, ultimately advancing safety performance in civil engineering projects. Background Large diameter tunnel construction presents significant safety challenges. Traditional methods often fall short of effectively predicting and mitigating risks. This study addresses these gaps by integrating robust optimization and machine learning (ML) approaches with Building Information Modeling (BIM) technology. By acquiring and preprocessing diverse datasets, implementing feature engineering, and employing ML algorithms, the study aims to enhance risk prediction and safety measures in tunnel construction projects. Objective The objective of this study is to improve safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). This involves acquiring and preprocessing diverse datasets, using feature engineering to extract key parameters, and applying ML algorithms like SVM, decision trees, ANN, and random forests to predict and mitigate risks, ultimately enhancing safety performance in civil engineering projects. Methods The study's methods include acquiring and preprocessing various datasets (geological, structural, environmental, operational, historical, and simulation). Feature engineering techniques are used to extract key safety parameters for tunnels. Machine learning algorithms, such as decision trees, support vector machines (SVM), artificial neural networks, and random forests, are employed to analyze the data and predict construction risks. The SVM algorithm, with a 98.76% accuracy, is the most reliable predictor. Results The study found that the Support Vector Machine (SVM) algorithm was the most accurate predictor of risks in large diameter tunnel construction, achieving a 98.76% accuracy rate. Other models, such as decision trees, artificial neural networks, and random forests, also performed well, validating the effectiveness of ML-based solutions for risk assessment and mitigation. These predictive models enable stakeholders to monitor construction, allocate resources, and implement preventative measures effectively. Conclusion The study concludes that integrating machine learning (ML) approaches with Building Information Modeling (BIM) significantly improves safety in large diameter tunnel construction. The Support Vector Machine (SVM) algorithm, with 98.76% accuracy, is the most reliable predictor of risks. Other models, like decision trees, artificial neural networks, and random forests, also perform well, validating ML-based solutions for risk assessment. Adopting these ML approaches enhances safety performance and resource management in civil engineering projects.

Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) contacts. In this contribution, we demonstrate that machine learning (ML) and artificial intelligence (AI) approaches (support vector machines, Gaussian process regressions, and artificial neural networks) can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations, respectively. For this purpose, we use data from EHL simulations based upon the full-system finite element (FE) solution and a Latin hypercube sampling. We verify that the original input data are required to train ML approaches to achieve coefficients of determination above 0.99. It is revealed that the architecture of artificial neural networks (neurons per layer and number of hidden layers) and activation functions influence the prediction accuracy. The impact of the number of training data is exemplified, and recommendations for a minimum database size are given. We ultimately demonstrate that artificial neural networks can predict the locally-resolved film thickness values over the contact domain 25-times faster than FE-based EHL simulations (R2 values above 0.999). We assume that this will boost the use of ML approaches to predict EHL parameters and traction losses in multibody system dynamics simulations.

Cross-border corruption and the illicit movement of financial assets, referred to as illicit financial flows (IFFs), have a strongly deleterious effect on the economies of developing nations. Over the past 20 years, there has been a concerted international effort to mitigate cross-border corruption; however, the most important economic and political factors leading to IFFs are unclear. In this work, we used multiple machine learning (ML) approaches—including linear regression, logistic regression, random forests, and neural networks—to predict the levels of corruption using various economic and political measures from the years 2009 to 2018. Furthermore, to make clear the relative importance of these factors, we used several ML model interpretation tools. We hypothesized that the artificial neural network (ANN) machine learning model can most effectively predict and explain IFFs in developing countries using economic and political indicators. Out of the various regression ML models, the ANN had the most success in predicting the IFFs, with a Pearson correlation coefficient of 0.97. The most important features, as quantified using Shapley values from the ANN and the feature importances of the random forest models were: aid percent of gross national income, population, human development indicator income, and government efficiency. Taken together, these models and their interpretation provide a method for predicting the IFFs as well as the features that drive them, enabling policymakers to focus on these factors to decrease corruption.

Machine Learning Approaches in Smart Health

Artificial intelligence and machine learning (ML) approaches have recently been getting much of researchers’ attention. The growing interest in these methods results from the fast development of machine learning algorithms in the last few years, especially artificial neural networks. In this review, we pay attention to the need and benefits that ML approaches can bring to tissue engineering (TE). We critically evaluate the possibilities of using the ML approaches in the tissue engineering field. We consider various paths of its utility in the TE, such as scaffold design, predicting the biological response to the scaffold, optimizing drug delivery approaches, supporting image analysis, and modeling scaffold in vivo performance. The current status of ML implementation is presented and supported by many study examples. On the other hand, we analyze the present difficulties and challenges in implementing ML approaches to tissue engineering, including the quality of published data, databases and repositories availability, the need for experiment and results publishing standardization, and ethical issues. Additionally, we assess the available natural language processing tools that could support TE research.Graphical abstract

BackgroundMalaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters.MethodsWe obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers.ResultsThe multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location.ConclusionThe study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.

Soil-cement mixtures reinforced with fibres are an alternative method of chemical soil stabilisation in which the inherent disadvantage of low or no tensile or flexural strength is overcome by incorporating fibres. These mixtures require a significant amount of time and resources for comprehensive laboratory characterisation, because a considerable number of parameters are involved. Therefore, the implementation of a Machine Learning (ML) approach provides an alternative way to predict the mechanical properties of soil-cement mixtures reinforced with fibres. In this study, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), and Multiple Regression (MR) algorithms were trained for predicting the elastic modulus of soil-cement mixtures reinforced with fibres. For ML algorithms training, a dataset of 121 records was used, comprising 16 properties of the composite material (soil, binder, and fibres). ANN and RF showed a promising determination coefficient (R2 ≥ 0.93) on elastic modulus prediction. Moreover, the results of the proposed models are consistent with the findings that the fibre and binder content have a significant effect on the elastic modulus.

Accurate forecasting of solid waste quantities is essential for sustainable waste management planning, yet limited research exists in this area. This study develops a framework to forecast solid waste generation, disposal, and diversion quantities using three machine learning (ML) approaches: artificial neural networks (ANNs), support vector machines (SVMs), and multiple linear regression (MLR) models. The forecasting framework is based on 12 socioeconomic variables, the values of which were derived from publicly available data sources. Projections for 2023 to 2050 were developed considering data preprocessing, training, and testing, to create reliable datasets. Correlation analysis was used to rank predictor and response variables, and statistical tests were conducted to identify heteroscedasticity and linear relationships. A case study was conducted for Canada and four provinces: Alberta (AB), British Columbia (BC), Ontario (ON), and Quebec (QC). The results show that ML algorithms predict solid waste effectively, achieving coefficients of determination (R2) of 99.9% with ANNs and 98.6% with SVMs. The total waste generation for Canada, forecast through ANNs, SVMs, and MLRs, increased by 18.29%, 22.45%, and 22.61%, respectively, in the 28 years from 2023 to 2050. In 2050, the projected values of waste generation using the three methods were 43.67, 45.14, and 44.47 million tonnes, respectively, in Canada. ANN forecasts for 2050 project 7.75 million tonnes in AB, 5.36 in BC, 17.85 in ON, and 8.81 in QC. Waste generation is increasing with increasing population size. The method developed here can be used globally with appropriate data adjustments. The results can help in policy development and decision making.

ObjectivesIn Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application.MethodsIn a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores.ResultsOf the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF.ConclusionsThe ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

The use of Machine Learning (ML) approaches to design anomaly-based network intrusion detection systems (A-NIDS) has been attracting growing interest due to, first, the ability of an A-NIDS to detect unpredictable and previously unseen network attacks, and second, the efficiency and accuracy of ML techniques to classify normal and malicious network traffic compared to other approaches. In this paper, we provide a comprehensive experimental evaluation of various ML approaches including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), on a recently published benchmark dataset called UNSW-NB15 considering both binary and multi-class classification. Throughout the experiments, we show that ANN is more accurate and has fewer false alarm rates (FARs) compared to other classifiers, which makes Deep Learning (DL) approaches a good candidate compared to shallow learning for future research. Moreover, we conducted our experiments in a way to be served as a benchmark results since our used approaches are trained and tested on the configuration deliberately provided by the authors of UNSW-NB15 dataset for the purpose of direct comparison.