Road accidents pose significant concerns globally. They lead to large financial losses, injuries, disabilities, and societal challenges. Accurate and timely accident data is essential for predicting and mitigating these events. This paper presents a novel framework named 'Durghotona GPT' that integrates web scraping and Large Language Models (LLMs) to automate the generation of comprehensive accident datasets from prominent national dailies in Bangladesh. The authors collected accident reports from three major newspapers: Prothom Alo, Dhaka Tribune, and The Daily Star. The collected news was then processed using the newest available LLMs: GPT-4, GPT-3.5, and Llama-3. The framework efficiently extracts relevant information, categorizes reports, and compiles detailed datasets. Thus, this framework overcomes limitations of manual data collection methods such as delays, errors, and communication gaps. The authors' evaluation demonstrates that Llama-3, an open-source model, performs comparably to GPT-4. It achieved 89% accuracy in the authors' evaluation. Therefore, it can be considered a cost-effective alternative for similar tasks. The results suggest that the framework developed by the authors can drastically enhance the quality and availability of accident data. As a result, it can support critical applications in traffic safety analysis, urban planning, and public health. The authors also developed an interface for 'Durghotona GPT' for ease of use as part of this paper. Future work will focus on expanding data collection methods and refining LLMs to further increase dataset accuracy and applicability.

Road traffic accidents (RTA) pose a significant public health threat worldwide, leading to considerable loss of life and economic burdens. This is particularly acute in developing countries like Bangladesh. Building reliable models to forecast crash outcomes is crucial for implementing effective preventive measures. To aid in developing targeted safety interventions, this study presents a machine learning-based approach for classifying fatal and non-fatal road accident outcomes using data from the Dhaka metropolitan traffic crash database from 2017 to 2022. Our framework utilizes a range of machine learning classification algorithms, comprising Logistic Regression, Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, LightGBM, and Artificial Neural Network. We prioritize model interpretability by employing the SHAP (SHapley Additive exPlanations) method, which elucidates the key factors influencing accident fatality. Our results demonstrate that LightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The global, local, and feature dependency analyses are conducted to acquire deeper insights into the behavior of the model. SHAP analysis reveals that casualty class, time of accident, location, vehicle type, and road type play pivotal roles in determining fatality risk. These findings offer valuable insights for policymakers and road safety practitioners in developing countries, enabling the implementation of evidence-based strategies to reduce traffic crash fatalities.

This research project delves into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, and multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple and logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time-series forecasting. With this approach, we've achieved superior predictive accuracy, marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast.

Road safety is a crucial issue that affects not only the individuals involved in road accidents, but also society as a whole. The cost of road accidents in terms of human lives lost, physical and emotional suffering, and financial losses is enormous. Thus, it is important to understand the factors that contribute to road accidents and to develop strategies to reduce the number and severity of these incidents. One of the most important steps in this process is the identification of "black spots," areas where the number of accidents is significantly higher compared to other parts of the road network. The identification of black spots is crucial for prioritizing road safety interventions and evaluating their effectiveness in reducing the number of accidents. These events can range from minor incidents, such as fender benders, to serious crashes, resulting in fatalities or severe injuries. Thus, identifying these areas provides insights into the underlying causes of these accidents. For example, black spot analysis can reveal the presence of road design or infrastructure issues that may contribute to accidents, such as poor lighting, confusing road signs, and a lack of pedestrian crossings.

Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.

Over the past few years, the topic of Artificial Intelligence has been on the lips of many people around the globe. The possibilities of AI-powered systems are limitless, which is the reason for its heavy demand and interest. The infusion of AIs into various sectors of the economy would not just create a fast, efficient, and effective working process but will also aid in ensuring safety and accountability, depending on how it is utilized. Several AI innovations have emerged in recent years to aid in everyday activities and unique platforms such as SafetyConnect have risen to help unlock the world of safe driving for employees of large enterprises. SafetyConnect is an AI-powered Field Force Driving and Work Safety Suit for enterprises.

This article was published as a part of the Data Science Blogathon. This is a multiclass classification project to classify the severity of road accidents into three categories. This project is based on real-world data, and the dataset is also highly imbalanced. There are three types of injuries in a target variable: minor, severe, and fatal. Road accidents are the major cause of unnatural deaths around the world.

Artificial Intelligence is becoming an integral part of many organizations' business plans. Already the journey of digital transformation has catapulted thanks to Machine Learning and Artificial Intelligence and because of the pandemic situation. The full scale of the impact that giving machines the ability to make decisions – and therefore enable decision-making to take place far more quickly and accurately than could ever be done by humans – is very difficult to conceive right now. But one thing we can be certain of is that in 2022 breakthroughs and new developments will continue to push the boundaries of what's possible. According to Sundar Pichai, the CEO of Google, AI will transform how we lead our lives and revamp many industries, including healthcare, education, and manufacturing.

If we look at the last couple of years, we have seen a significant leap in the way Artificial Intelligence is becoming an integral part of many organizations' business plans. Already the journey of digital transformation has catapulted thanks to Machine Learning and Artificial Intelligence and because of the pandemic situation, we saw significant innovation in the technology front, which will reach new heights in the year 2022 and further. As strongly claimed by Sundar Pichai, CEO of Google Inc, the impact of artificial intelligence will be far greater than fire and electricity on humanity. Well, it might sound a bit exaggerated but what it implies is that the year 2022 is going to see new developments in this space and it will constantly create new benchmarks. It is a looming fear that machines or robots will eventually replace the human workforce and may even render certain roles obsolete or redundant.

"Even primitive autonomous technology that exists today is better than an average human driver on the road" Recent moves by big players signal at the potential of India's AV ecosystem. Tata Elxsi has created a full-stack IP called AutonomAI to accelerate programs for AVs, and Intel's decision to gather data on traffic patterns to create algorithms to be used in India and overseas for autonomous driving. Moreover, a large number of AV startups have come up the ladder, especially in the last six years, namely: Swaayatt (2015), AutoNxt (2016), Ati Motors and Flux Auto (2017), Flo Mobility (2019), Minus Zero (2020) etc. Autonomous vehicles (AVs) startups in India, including Swaayatt Robots, Ati Motors, Netradyne, have raised substantial funding recently. Saurabh Chandra, Co-Founder, Ati Motors, said: "There is a confluence of factors due to the pandemic: workforce management has become challenging, prices are rising, making automation more attractive while technology is getting better and cheaper. There is an explosion of demand in the eCommerce space that can't be addressed by simply adding people; technology is the only way to handle the scale." India is a huge market.