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Author : Pei Zhang
Publisher :
ISBN 13 :
Total Pages : 154 pages
Book Rating : 4.:/5 (646 download)
Download or read book Identification and Prediction of Highway Accidents Using Decision Trees written by Pei Zhang and published by . This book was released on 2005 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mirza Ahammad Sharif
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)
Download or read book Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms written by Mirza Ahammad Sharif and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study's selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase.
Author : National Research Council (U.S.). Transportation Research Board
Publisher :
ISBN 13 :
Total Pages : 148 pages
Book Rating : 4.X/5 (4 download)
Download or read book Highway and Traffic Safety written by National Research Council (U.S.). Transportation Research Board and published by . This book was released on 2000 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).
Author : Oded Z Maimon
Publisher : World Scientific
ISBN 13 : 9814590096
Total Pages : 328 pages
Book Rating : 4.8/5 (145 download)
Download or read book Data Mining With Decision Trees: Theory And Applications (2nd Edition) written by Oded Z Maimon and published by World Scientific. This book was released on 2014-09-03 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
Author : Biswajeet Pradhan
Publisher : Springer
ISBN 13 : 3030103749
Total Pages : 165 pages
Book Rating : 4.0/5 (31 download)
Download or read book Laser Scanning Systems in Highway and Safety Assessment written by Biswajeet Pradhan and published by Springer. This book was released on 2019-04-02 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.
Author : Matthew Colin Ruder
Publisher :
ISBN 13 : 9781303443756
Total Pages : pages
Book Rating : 4.4/5 (437 download)
Download or read book Identifying Collision Parameters in Highway Work Zones Collisions Using Classification and Regression Trees written by Matthew Colin Ruder and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Highway safety modeling represents a means to study interventions that can potentially reduce the thousands of collisions that result in injuries and fatalities each year. Statistical modeling of roadways can identify the roadways where safety interventions are most needed or where the largest effects of such interventions could be felt. Work zone areas are necessary to repair and build new roads that, unfortunately, can increase the risk of collision. Prior to a work zone becoming active, statistical models can estimate the numbers of collisions that will occur as a result of these work zones. Likewise the effectiveness of safety interventions prior to deployment can be used to estimate the reduction in collisions as a result of the interventions.The purpose of this research was to develop a method to estimate the numbers and types of collisions that occur on California roadways. This was achieved by using the classification and regression tree (CART) modeling method to identify key collision factors that result in injuries and fatalities by utilizing data from highway databases. After the factors had been identified, an empirical Bayesian (EB) model was then used to find the number of collisions that would occur on a given road. The CART method provides a weighting factor that can be used to find the number of each type of collision that will occur. Certain types of collisions are more likely to result in a serious injury or fatality can be identified though this method. Cost-benefit analysis could then be used to determine where targeted deployment of California Highway Patrolmen (COZEEP/MAZEEP) would be most effective in reducing these types of collisions.Based on the analysis of the combined CART and EB methods, the types of collisions that are more likely to cause injury/fatality on California highways were identified as well as factors that influence collisions. The methodology developed represents a significant step in highway analysis as it combined information from several established highway databases while also utilizing several highway safety research methods. More accurate prediction models can be used to evaluate where safety interventions, such as COZEEP/MAZEEP, would have the largest impact prior to implementation. These collision models can be used to find the more optimal locations to deploy said safety interventions. Three of four highways identified types of collisions that lead to injury/fatality collisions. Only one highway, I-680, identified the underlying primary factors that are likely to lead to injury/fatality collisions. Cost-benefit analysis was used to calculate the effective cost of deployment of officers based on the numbers and types of collisions reduced. This research provides the framework that can be applied to other roadways within California.
Author : Thomas M. England
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)
Download or read book Road Traffic Crash Severity Prediction Using Multi-State Data written by Thomas M. England and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The socioeconomic burden of road traffic crashes is immense. Safer roads and vehicular mechanisms to reduce distracted driving help reduce collisions. Additionally, computational models can be used to understand the reasons for crashes and devise interventions. We study models predicting the severity of a crash based on the data reported at the crash scene. Many U.S. states have developed traffic safety programs to make the anonymized crash data publicly available. These datasets aid researchers in the creation of predictive models for crashes. While many states make data from collisions publicly available, each state reports data differently. There is a lack of standardization. As a result, it is difficult for researchers to develop machine learning algorithms to process data from multiple states without adequate preprocessing. Currently, the vast majority of projects in this field of study utilize a dataset of a single city, road, or state. This limits the use of the developed model to a region. This project aims to create a large crash database that will allow researchers to develop algorithms that utilize data from across the country. Additionally, we want to examine if the use of data from multiple states is effective in increasing the accuracy of machine learning models. In order to achieve these goals, we develop software to find common data categories from state reports and combine them into one large dataset. The data categories were selected based on reports from previous projects that identified variables having a large impact on model accuracy. In order to test the effectiveness of the new multi-state dataset, we used two models (neural network-based and decision tree-based) to predict crash injury severity. We trained and tested these models on datasets from a single state, combined two-state datasets, and a combined multi-state dataset. The results of this research reveal that there is a drop in accuracy when data from multiple states are combined. This trend is present in both the models tested, with the trend being more pronounced in the decision tree. There are some cases in the neural network model where multi-state data lead to a higher accuracy compared to the single-state experiments. We also observe a downward trend between neural network accuracy and the distance between the states present in the dataset. This implies that the closer the states are together geographically, the better the accuracy will be using the neural network model. In the decision tree model, there is a positive correlation between overall accuracy and the number of features present in the dataset. This observation means that the more features states have in common, the better the accuracy will be for a decision tree classifier. The software artifacts from this project are open-sourced.
Author : V.A Profillidis
Publisher : Elsevier
ISBN 13 : 0128115149
Total Pages : 500 pages
Book Rating : 4.1/5 (281 download)
Download or read book Modeling of Transport Demand written by V.A Profillidis and published by Elsevier. This book was released on 2018-10-23 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand Provides a theoretical analysis and formulations that are clearly presented for ease of understanding Covers analysis for all modes of transportation Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results
Author : Jiuping Xu
Publisher : Springer
ISBN 13 : 3030212483
Total Pages : 837 pages
Book Rating : 4.0/5 (32 download)
Download or read book Proceedings of the Thirteenth International Conference on Management Science and Engineering Management written by Jiuping Xu and published by Springer. This book was released on 2019-06-19 with total page 837 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 13th International Conference on Management Science and Engineering Management (ICMSEM 2019), which was held at Brock University, Ontario, Canada on August 5–8, 2019. Exploring the latest ideas and pioneering research achievements in management science and engineering management, the respective contributions highlight both theoretical and practical studies on management science and computing methodologies, and present advanced management concepts and computing technologies for decision-making problems involving large, uncertain and unstructured data. Accordingly, the proceedings offer researchers and practitioners in related fields an essential update, as well as a source of new research directions.
Author : Amit Kumar
Publisher : Springer Nature
ISBN 13 : 981157961X
Total Pages : 1561 pages
Book Rating : 4.8/5 (115 download)
Download or read book ICCCE 2020 written by Amit Kumar and published by Springer Nature. This book was released on 2020-10-11 with total page 1561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of research papers and articles presented at the 3rd International Conference on Communications and Cyber-Physical Engineering (ICCCE 2020), held on 1-2 February 2020 at CMR Engineering College, Hyderabad, Telangana, India. Discussing the latest developments in voice and data communication engineering, cyber-physical systems, network science, communication software, image and multimedia processing research and applications, as well as communication technologies and other related technologies, it includes contributions from both academia and industry. This book is a valuable resource for scientists, research scholars and PG students working to formulate their research ideas and find the future directions in these areas. Further, it may serve as a reference work to understand the latest engineering and technologies used by practicing engineers in the field of communication engineering.
Author : Thomas Véran
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Efficient and Interpretable Crash Prediction Models written by Thomas Véran and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Worldwide, highway accidents have important social and financial impacts. To reduce their frequency and gravity, crash prediction models (CPM) are used to identify hazardous roadway segments and to provide actionable clues about the associated risk factors. CPM are either interpretable-by-design parametric statistical models, in particular generalized linear models (GLM), or machine learning models with a large number of parameters without associated uncertainty estimates (e.g., ensemble of decision trees, support vector machine ...). When pondering high stake decisions, such as in the context of highway safety, field experts expect predictive models to be both effective and glass-box interpretable to help them deploy preventive safety actions. As such, we contribute to enhancing the predictive performance of parametric models while maintaining their interpretability. Our main contributions aim to achieve this goal in two steps. First, we introduce a supervised method to discover a partition of the original observations and build a hierarchical model above it. Second, we introduce two algorithmic approaches (viz., a polynomial neural network, and an extension of multi-objective symbolic regression) to discover highly discriminant non-linear transforms of the original variables. The former can handle correlations among groups of observations which usually lead to improvements in the quality of the models' predictions and of their interpretation. The latter, while remaining simple (e.g. first-order interactions), allow the models to capture more of the variability in the dependent variable. Experiments have been conducted on a highway safety dataset and on more than ten public datasets covering classification and regression tasks. They show promising results with our contributions outperforming traditional glass-box interpretable models while getting close to the best non-parametric models. Finally, we illustrate the benefits of our approach by introducing, on a realistic case study, an application we designed for highway safety experts.
Author : Vijendra Singh
Publisher : Springer Nature
ISBN 13 : 9811579075
Total Pages : 559 pages
Book Rating : 4.8/5 (115 download)
Download or read book Computational Methods and Data Engineering written by Vijendra Singh and published by Springer Nature. This book was released on 2020-11-04 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering (ICMDE 2020), held at SRM University, Sonipat, Delhi-NCR, India. Focusing on cutting-edge technologies and the most dynamic areas of computational intelligence and data engineering, the respective contributions address topics including collective intelligence, intelligent transportation systems, fuzzy systems, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing.
Author : Esmaeil Zarei
Publisher : Springer Nature
ISBN 13 : 303162470X
Total Pages : 537 pages
Book Rating : 4.0/5 (316 download)
Download or read book Safety Causation Analysis in Sociotechnical Systems written by Esmaeil Zarei and published by Springer Nature. This book was released on 2024 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Prasenjit Chatterjee
Publisher : Springer Nature
ISBN 13 : 981198493X
Total Pages : 925 pages
Book Rating : 4.8/5 (119 download)
Download or read book Computational Intelligence for Engineering and Management Applications written by Prasenjit Chatterjee and published by Springer Nature. This book was released on 2023-04-29 with total page 925 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022). This book emphasizes applications of computational intelligence including machine intelligence, data analytics, and optimization algorithms for solving fundamental and advanced engineering and management problems. This book serves as a valuable resource for researchers, industry professionals, academicians, and doctoral scholars in engineering, production, thermal, materials, design, computer engineering, natural sciences, and management who work on computational intelligence. The book also serves researchers who are willing to use computational intelligence algorithms in real-time applications.
Author : Shengzhao Long
Publisher : Springer Nature
ISBN 13 : 9811947864
Total Pages : 740 pages
Book Rating : 4.8/5 (119 download)
Download or read book Man-Machine-Environment System Engineering written by Shengzhao Long and published by Springer Nature. This book was released on 2022-08-20 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Man-Machine-Environment System Engineering: Proceedings of the 22nd Conference on MMESE are an academic showcase of the best papers selected from more than 500 submissions, introducing readers to the top research topics and the latest developmental trends in the theory and application of MMESE. This proceedings are interdisciplinary studies on the concepts and methods of physiology, psychology, system engineering, computer science, environment science, management, education, and other related disciplines. Researchers and professionals who study an interdisciplinary subject crossing above disciplines or researchers on MMESE subject will be mainly benefited from this proceedings MMESE primarily focuses on the relationship between Man, Machine and Environment, studying the optimum combination of man-machine-environment systems. In this system, “Man” refers to working people as the subject in the workplace (e.g. operators, decision-makers); “Machine” is the general name for any object controlled by Man (including tools, machinery, computers, systems and technologies), and “Environment” describes the specific working conditions under which Man and Machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of the man-machine-environment systems are to ensure safety, efficiency and economy. The integrated and advanced science research topic Man-Machine-Environment System Engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”
Author : Nadihah Wahi
Publisher : Springer Nature
ISBN 13 : 9464630140
Total Pages : 510 pages
Book Rating : 4.4/5 (646 download)
Download or read book Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022) written by Nadihah Wahi and published by Springer Nature. This book was released on 2023-02-10 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. The ICMSS2022 is an international conference jointly organised by the Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia together with the Banasthali University, Jaipur, India. This international conference aims to give exposure and to bring together academicians, researchers and industry experts for intellectual growth. The ICMSS2022 serves as a platform for the scientific community members to exchange ideas and approaches, to present research findings, and to discuss current issues and topics related to mathematics, statistics as well as their applications. Objectives: to present the most recent discoveries in mathematics and statistics. to serve as a platform for knowledge and information sharing between experts from industries and academia. to identify and create potential collaboration among participants. The organising committee of ICMSS2022 welcomes all delegates to deliberate over various aspects related to the conference themes and sub-themes.
Author : Prayag Tiwari
Publisher : GRIN Verlag
ISBN 13 : 3668613079
Total Pages : 82 pages
Book Rating : 4.6/5 (686 download)
Download or read book Accident Analysis by Using Data Mining Techniques written by Prayag Tiwari and published by GRIN Verlag. This book was released on 2018-01-16 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2017 in the subject Computer Sciences - Industry 4.0, grade: 5.0/5.0, , course: Computer Science and Engineering, language: English, abstract: Accident data analysis is one of the prime interests in the present era. Analysis of accident is very essential because it can expose the relationship between the different types of attributes that commit to an accident. Road, traffic and airplane accident data have different nature in comparison to other real world data as accidents are uncertain. Analyzing diverse accident dataset can provide the information about the contribution of these attributes which can be utilized to deteriorate the accident rate. Nowadays, Data mining is a popular technique for examining the accident dataset. In this study, Association rule mining, different classification, and clustering techniques have been implemented on the dataset of the road, traffic accidents, and an airplane crash. Achieved result illustrated accuracy at a better level and found many different hidden circumstances that would be helpful to deteriorate accident ratio in near future.
