Accident prediction using machine learning:analyzing weather conditions, and model performance

Abstract. The primary focus of this study was to investigate the impact of weather and road conditions on the severity of accidents and to determine the feasibility of machine learning models in accurately predicting the likelihood of such incidents. The research was centered on two key research questions. Firstly, the study examined the influence of weather and road conditions on accident severity and identified the most related factors contributing to accidents. We utilized an open-source accident dataset, which was preprocessed using techniques like variable selection, missing data elimination, and data balancing through the Synthetic Minority Over-sampling Technique (SMOTE). Chi-square statistical analysis was performed, suggesting that all weather-related variables are more or less associated with the severity of accidents. Visibility and temperature were found to be the most critical factors affecting the severity of road accidents. Hence, appropriate measures such as implementing effective fog dispersal systems, heatwave alerts, or improved road maintenance during extreme temperatures could help reduce accident severity. Secondly, the research evaluated the ability of machine learning models including decision trees, random forests, naive bayes, extreme gradient boost, and neural networks to predict accident likelihood. The models’ performance was gauged using metrics like accuracy, precision, recall, and F1 score. The Random Forest model emerged as the most reliable and accurate model for predicting accidents, with an overall accuracy of 98.53%. The Decision Tree model also showed high overall accuracy (95.33%), indicating its reliability. However, the Naive Bayes model showed the lowest accuracy (63.31%) and was deemed less reliable in this context. It is concluded that machine learning models can be effectively used to predict the likelihood of accidents, with models like Random Forest and Decision Tree proving the most effective. However, the effectiveness of each model may vary depending on the dataset and context, necessitating further testing and validation for real-world implementation. These findings not only provide insight into the factors affecting accident severity but also open a promising avenue in employing machine learning techniques for proactive accident prediction and mitigation. Future studies can aim to refine the models further and potentially integrate them into traffic management systems to enhance road safety

Providing drivers with road-edge information to reduce road departure crashes in a military vehicle fleet

This work was conducted for a phase II SBIR project for the U.S. Army (SBIR topic A05-222)First year report: December 1, 2006 to December 31, 2007A leading cause of military vehicle rollover crashes is that one or more wheels move into an area where the terrain falls away steeply or disappears, leading to vehicle rollover. Vehicle-mounted sensors will soon be capable of sensing such hazards in real time. This report addresses the design of a driver interface to provide information about such hazards in a timely and cogent manner in order to allow attentive, distracted, or drowsy drivers enough time and information to avoid the hazard. An interface that consists of an auditory warning and an optional supplementary overlay of the hazard on a driver’s eye view of the roadway is recommended. A set of equations are developed that indicate when the driver must begin applying either a pre-determined level of braking or a pre-determined level of added lateral acceleration to avoid a perceived hazard.Physical Sciences Inc.http://deepblue.lib.umich.edu/bitstream/2027.42/64997/1/102507.pd

Use of Machine Learning and Natural Language Processing to Enhance Traffic Safety Analysis

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies within and outside traffic safety domain.The primary source for traffic safety analysis is the structure (also called tabular) data collected from crash reports. However, structure data may be insufficient because of missing information, incomplete sequence of events, misclassified crash types, among many issues. Crash narratives, a form of free text recorded by police officers to describe the unique aspects and circumstances of a crash, are commonly used by safety professionals to supplement structure data fields. Due to its unstructured nature, engineers have to manually review every crash narrative. Thanks to the rapid development in natural language processing (NLP) and machine learning (ML) techniques, text mining and analytics has become a popular tool to accelerate information extraction and analysis for unstructured text data. The primary objective of this dissertation is to discover and develop necessary tools, techniques, and algorithms to facilitate traffic safety analysis using crash narratives. The objectives are accomplished in three areas: enhancing data quality by recovering missed crashes through text classification, uncovering complex characteristics of collision generation through information extraction and pattern recognition, and facilitating crash narrative analysis by developing a web-based tool. At first, a variety of NoisyOR classifiers were developed to identify and investigate work zone (WZ), distracted (DD), and inattentive (ID) crashes. In addition, various machine learning (ML) models, including multinomial naive bayes (MNB), logistic regression (LGR), support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and gated recurrent unit (GRU), were developed and compared with NoisyOR. The comparison shows that NoisyOR is simple, computationally efficient, theoretically sound, and has one of the best model performances. Furthermore, a novel neural network architecture named Sentence-based Hierarchical Attention Network (SHAN) was developed to classify crashes and its performance exceeds that of NoisyOR, GRU, Hierarchical Attention Network (HAN), and other ML models. SHAN handled noisy or irrelevant parts of narratives effectively and the model results can be visualized by attention weight. Because a crash often comprises a series of actions and events, breaking the chain of events could prevent a crash from reaching its most dangerous stage. With the objectives of creating crash sequences, discovering pattern of crash events, and finding missing events, the Part-of-Speech tagging (PT), Pattern Matching with POS Tagging (PMPT), Dependency Parser (DP), and Hybrid Generalized (HGEN) algorithms were developed and thoroughly tested using crash narratives. The top performer, HGEN, uses predefined events and event-related action words from crash narratives to find new events not captured in the data fields. Besides, the association analysis unravels the complex interrelations between events within a crash. Finally, the crash information extraction, analysis, and classification tool (CIEACT), a simple and flexible online web tool, was developed to analyze crash narratives using text mining techniques. The tool uses a Python-based Django Web Framework, HTML, and a relational database (PostgreSQL) that enables concurrent model development and analysis. The tool has built-in classifiers by default or can train a model in real time given the data. The interface is user friendly and the results can be displayed in a tabular format or on an interactive map. The tool also provides an option for users to download the word with their probability scores and the results in csv files. The advantages and limitations of each proposed methodology were discussed, and several future research directions were outlined. In summary, the methodologies and tools developed as part of the dissertation can assist transportation engineers and safety professionals in extracting valuable information from narratives, recovering missed crashes, classifying a new crash, and expediting their review process on a large scale. Thus, this research can be used by transportation agencies to analyze crash records, identify appropriate safety solutions, and inform policy making to improve highway safety of our transportation system

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning

International audienceUrban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or a Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs a higher computational cost. This paper proposes a novel framework that combines the descriptive strength of the Gaussian Mixture Model with the high-performance classification capabilities of the Support Vector Classifier. A new approach is presented that uses the mean vectors obtained from the GMM model as input to the SVC. Experimental results show that the approach compares very favorably with baseline statistical methods