Predictive analytics in agribusiness industries

Agriculturally related industries are routinely among the most hazardous work environments. Workplace injuries directly impact labor-market outcomes including income reduction, job loss, and health of the injured workers. In addition to medical and indemnity costs, workplace incidents include indirect costs such as equipment damage and repair, incident investigation time, training new personnel for replacement of the injured ones, an increase in insurance premiums for the year following the incidents, a slowdown of production schedules, damage to companies’ reputation, and lowering the workers’ motivation to return to work. The main purpose of incident analysis is the derivation and development of preventative measures from injury data. Applying proper analytical tools aimed at discovering the causes of occupational incidents is essential to gain useful information that contributes in preventing those incidents in future. Insight gained from the analyses of workers’ compensation data can efficiently direct preventative activities at high-risk industries. Since incidents arise from a combination of factors rather than a single cause, research on occupational incidents must go deeper into identifying the underlying causes and their relationship through applying more comprehensive analyses. Therefore, this study aimed at identifying underlying patterns in occupational injury occurrence and costs using data mining and predictive modeling techniques instead of traditional statistical methods. Utilizing a workers’ compensation claims dataset, the objectives of this study were to: investigate the use of predictive modeling techniques in forecasting future claims costs based on historical data; identify distinctive patterns of high-cost occupational injuries; and examine how well machine learning methods work in finding the predictive relationship between factors influencing occupational injuries and workers’ compensation claims occurrence and severity. The results lead to a better understanding of injury patterns, identification of prevalent causes of occupational injuries, and identification of high-risk industries and occupations. Therefore, various stakeholders such as policymakers, insurance companies, safety standard writers, and manufacturers of safety equipment can use the findings of the study to plan for remedial actions and revise safety standards. The implementation of safety measures by agribusiness organizations can prevent occupational injuries, save lives, and reduce the occurrence and cost of such incidents in agricultural work environments

ANALIZA KOLIZJI W RUCHU MIEJSKIM Z WYKORZYSTANIEM TECHNIK GŁĘBOKIEGO UCZENIA

Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents. This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam, and Gandhinagar in Vijayawada (India) from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniques are applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These models are trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8 946 ofiarami śmiertelnymi w 22 311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiar i ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkach z regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowych na śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tym wielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh

Reconciling Big Data and Thick Data to Advance the New Urban Science and Smart City Governance

Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation

Tennessee Highway Safety Office Highway Safety Plan FFY 2021

https://digitalcommons.memphis.edu/govpubs-tn-safety-homeland-security-highway-safety-office/1003/thumbnail.jp

Analysis of Car Accidents Causes in the USA

Over the past decade more people are opting in to purchase and own their own personal vehicle and in some instances several, which means that road accidents rates are doomed to increase. And this presents a challenge to the government, individuals and the collective community as car accidents are in most cases life threatening and a hazard to society. Thus, this paper aims to tackle this issue and dig deep to explore the main factors contributing to the increase of car accidents rate. The dataset used in this research is data collected from traffic accidents events captured by the department of transportation, law-enforcement agencies, and traffic cameras continuously in the United States from 2016 to 2020. Two models were performed to predict the impact of car accidents on road traffic, with a focus on the leading factors contributing to road accidents. Results showed that the main two factors affecting car accidents rate are traffic caused by work rush hour and population density. Furthermore, this research can be used to create solutions to limit and decrease car accidents in cities, such as adopting the working from home concept, facilitating the ownership of self-driving vehicles, creating a seamless public transportation infrastructure, and distributing rush hours throughout the day to name a few

Tennessee Highway Safety Office Highway Safety Plan FFY 2022

https://digitalcommons.memphis.edu/govpubs-tn-safety-homeland-security-highway-safety-office/1001/thumbnail.jp

Prediction of Crash Injury Severity in Florida's Interstate-95

Drivers can sustain serious injuries in traffic accidents. In this study, traffic crashes on Florida's Interstate-95 from 2016 to 2021 were gathered, and several classification methods were used to estimate the severity of driver injuries. In the feature selection method, logistic regression was applied. To compare model performances, various model assessment matrices such as accuracy, recall, and area under curve (AUC) were developed. The Adaboost algorithm outperformed the others in terms of recall and AUC. SHAP values were also generated to explain the classification model's results. This analytical study can be used to examine factors that contribute to the severity of driver injuries in crashes

Predictive Power of Criminal Background on Losses

Product and data science teams for the auto insurance industry have been trying to increase pricing segmentation with validated rating variables to decrease rate subsidization. The criminal background data availability provided a new behavior variable to test against insurance-based credit scores as a potential predictive variable in the generalized linear rating model. Criminal background was analyzed using a Poisson Log Linear model and other key insurance rating variables for predicting loss costs. The study supported the inclusion of the criminal background data in combination with insurance-based credit score as the variable’s addition could improve the overall fit of the predictive model. The study also acknowledged there was a statistically significant association between criminal background and insurance-based credit score, but the overall size of the effect was small and weak. The overall contribution of value criminal background variable needs to be considered with a full rating dataset to determine if other, less powerful variables could be removed from the generalized linear to reduce the overall model complexity

Tennessee Highway Safety Office Annual Report FFY 2018

https://digitalcommons.memphis.edu/govpubs-tn-safety-homeland-security-highway-safety-office/1008/thumbnail.jp

Short-term crash risk prediction considering proactive, reactive, and driver behavior factors

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events. The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity