Predicting Automobile Accident Severity and Hotspots Using Multinomial Logistic Regression

Title: Predicting automobile accident severity and hotspots using multinomial logistic regression. Americans are now driving more than ever [1]. In 2010, close to 33,000 lives were lost and another estimated 3.9 million people were injured in automobile accidents; all things considered, these accidents accounted for $836 billion in damages [2]. Since then, the rate of automobile-related deaths per 100 million miles traveled has not shown signs of improvement [3]. This research expands upon a previous year’s poster presented at the South Dakota State University Data Science Symposium 2019 [4]. While the previous research focuses on a data visualization of automobile accident hotspots on a map based on the severity and frequency of accidents, this research aims to train a multinomial logistic regression machine learning model using data related to weather conditions, speed limit, and GPS coordinates to predict the severity of automobile accidents. The development of such a machine learning model can help inform emergency services better manage resources in anticipation of potential automobile accidents based on prevailing weather conditions, speed limit along a stretch of road, and location data. An updated version of the previous dataset will be used. This dataset contains approximately 1.5 million automobile accident data points, collected over a span of over four years, from February 2016 to December 2020. References [1] US Department of Transportation. Federal Highway Administration. (May, 2019). Strong economy has Americans driving more than ever before. Retrieved from https://cms8.fhwa.dot.gov/newsroom/strong-economy-has-americans-driving-more-ever [2] Blincoe, L. J., Miller, T. R., Zaloshnja, E., & Lawrence, B. A. (2015, May). The economic and societal impact of motor vehicle crashes, 2010. (Revised)(Report No. DOT HS 812 013). Washington, DC: National Highway Traffic Safety Administration. [3] National Center for Statistics and Analysis (2019, December). Early estimate of motor vehicle traffic fatalities for the first 9 months (Jan – Sep) of 2019. (Crash Stats Brief Statistical Summary. Report No. DOT HS 812 874). Washington, DC: Highway Traffic Safety Administration. [4] Identification of Automobile Accident Hotspots using Countrywide Traffic Accident Dataset, B. Z. Yang & S. Z. Sajal, Ph.D., Presented at 2020 South Dakota State University Data Science Symposium

ANALYSIS OF US ACCIDENTS AND SOLUTIONS

Reducing traffic accidents is an essential public safety challenge all over the world; therefore, accident analysis has been a subject of much research in recent decades. The objective of the project is to analyze the US accident data from 50 states to inform the US government agencies and the general public on trends and possible causes of traffic accidents and what could be done to reduce them. The analysis include number of accidents by year, number of accidents by state, best time to travel by month, day and hour, accident-prone area in each state, factors responsible of the accidents like weather, wind flow, temperature, location, etc., deaths in each state, age group of fatalities, drivers involved in accident, drivers age group, vehicles involved in accident, driver with alcohol consumption. The analysis platform is built using Tableau. Government agencies and the general public can leverage these insights and take a preventive measure which can reduce US accidents. The significant findings from the analysis are: (a) most of the accidents are happening in October, November, December; (b) nighttime is safe to travel, (c) a more substantial number of deaths are from drivers in the 20-35 age group; (d) weather, temperature, and location are the factors responsible for 9% of accidents, and (e) about 60% accidents are attributed to drunk driving. The main recommendations from the project focus on Infrastructure, Policy, Administrative, and Human behavior-related changes that can be implemented by the state and the federal government

GR-232 - Accident Prediction using Big Data Analysis using Ensemble Learning

Many country wide internal roads and national highways have dim lights or no street lights all over the world. We usually observe some turns on roads more prone to accidents than other places. In our paper we used Logistic Regression and Decision Trees that together build an Ensemble learning for predicting these accident zones. For validating the results, five evaluation metrics such as Accuracy, Precision, f-measures, Re-call and Area under curve are used. State of art model for US accident dataset gives F1 score of 57%. We are implementing using ensemble learning wherein logistic regression gives F1-score of 53%

Traffic incident duration prediction via a deep learning framework for text description encoding

Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by $60\%$ when compared to standard linear or support vector regression models, and a further $7\%$ improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System)

Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.Comment: In Proceedings of the 27th ACM SIGSPATIAL, International Conference on Advances in Geographic Information Systems (2019). arXiv admin note: substantial text overlap with arXiv:1906.0540

Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by identifying potential unsafe road conditions and taking well-informed actions to reduce the number of severe accidents. This study investigates the effectiveness of the Random Forest machine learning algorithm for predicting the severity of an accident. The model is trained on a dataset of accident records from a large metropolitan area and evaluated using various metrics. Hyperparameters and feature selection are optimized to improve the model's performance. The results show that the Random Forest model is an effective tool for predicting accident severity with an accuracy of over 80%. The study also identifies the top six most important variables in the model, which include wind speed, pressure, humidity, visibility, clear conditions, and cloud cover. The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a precision of 97.1%, and an F1 score of 87.3%. These results suggest that the proposed model has higher performance in explaining the target variable, which is the accident severity class. Overall, the study provides evidence that the Random Forest model is a viable and reliable tool for predicting accident severity and can be used to help reduce the number of fatalities and injuries due to road accidents in the United StatesComment: 15 page

Correlating Extreme Weather Conditions With Road Traffic Safety: A Unified Latent Space Model

The presence of extreme weather conditions is known to expose drivers to a higher risk to incur in road accidents. Quantifying the correlation between adverse weather conditions and road traffic safety is useful for several reasons such as planning preventive actions, managing vehicle fleets, and configuring alerting systems. However, since the risk of road accidents occurrences within a specific spatial region is influenced by several factors other than the weather conditions, quantifying the actual impact of adverse weather phenomena regardless of the effect of weather-unrelated conditions can be challenging. To tackle the aforesaid issue, this paper proposes to adopt a unified latent space model based on time series embeddings. Firstly, it encodes a subset of historical series reporting weather-related accident occurrences in specific risky areas into the high-dimensional vector representation. It also encodes the weather element measurements acquired by meteorological stations spread over the analyzed area. Then, to estimate the risk level of each region within the same spatial context it seeks the temporal risk patterns that are most similar to those observed in risky areas. The experiments carried out in a real case study confirm the applicability of the proposed approach

Analysis of Automated Vehicle Accidents Across the U.S.

Car Accidents are a significant public safety hazard to everyone all over the world. Recently, car manufacturers have tried to implement Automated Driving Systems into their vehicles to help drivers and decrease the accident rate. This culminating project analyzed whether Automated Driving Systems have decreased or increased the accident rate in the United States using automated vehicle data collected by the National Highway Traffic Safety Administration. The analysis platform was built using RStudio and Excel. The research questions were: Q1: What amount of autonomous vehicle accidents occurred in each state between 2021 and 2022? Q2: What manufacturer and maker have been involved in the most accidents? Q3: What month/year have accidents occurred the most? Q4: What were the factors surrounding these accidents? Q5: What solutions can we implement to reduce autonomous car road traffic collisions? The findings and conclusions are: Q1: More accidents occurred in 2021 and most were in the state of California. Q2: The manufacturing company, Waymo LLC, and the maker, Jaguar, have been involved in the most accidents. Q3: Most accidents occurred in November of 2021 with 19 crashes in total. Q4: The factors surrounding these accidents were most crashes occurred on the streets and intersections, on dry surfaces, with a posted speed limit of 25 miles per hour, and during the daylight. Q5: Solutions we can implement to reduce autonomous car road traffic collisions are building more railroads and trains, to partner with national programs to release road traffic information and create enhanced data systems to make necessary changes. Areas for further study include collecting data from other countries to further understand if they are also experiencing the same issues and what actions they have taken

Burden of injuries in Nepal, 1990–2017: Findings from the Global Burden of Disease Study 2017

Background: Nepal is a low-income country undergoing rapid political, economic and social development. To date, there has been little evidence published on the burden of injuries during this period of transition.Methods: The Global Burden of Disease Study (GBD) is a comprehensive measurement of population health outcomes in terms of morbidity and mortality. We analysed the GBD 2017 estimates for deaths, years of life lost, years lived with disability, incidence and disability-adjusted life years (DALYs) from injuries to ascertain the burden of injuries in Nepal from 1990 to 2017.Results: There were 16 831 (95% uncertainty interval 13 323 to 20 579) deaths caused by injuries (9.21% of all-cause deaths (7.45% to 11.25%)) in 2017 while the proportion of deaths from injuries was 6.31% in 1990. Overall, the injury-specific age-standardised mortality rate declined from 88.91 (71.54 to 105.31) per 100 000 in 1990 to 70.25 (56.75 to 85.11) per 100 000 in 2017. In 2017, 4.11% (2.47% to 6.10%) of all deaths in Nepal were attributed to transport injuries, 3.54% (2.86% to 4.08%) were attributed to unintentional injuries and 1.55% (1.16% to 1.85%) were attributed to self-harm and interpersonal violence. From 1990 to 2017, road injuries, falls and self-harm all rose in rank for all causes of death.Conclusions: The increase in injury-related deaths and DALYs in Nepal between 1990 and 2017 indicates the need for further research and prevention interventions. Injuries remain an important public health burden in Nepal with the magnitude and trend of burden varying over time by cause-specific, sex and age group. Findings from this study may be used by the federal, provincial and local governments in Nepal to prioritise injury prevention as a public health agenda and as evidence for country-specific interventions