Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7115 |
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| author | Lars Skaug Mehrdad Nojoumian Nolan Dang Amy Yap |
| author_facet | Lars Skaug Mehrdad Nojoumian Nolan Dang Amy Yap |
| author_sort | Lars Skaug |
| collection | DOAJ |
| description | Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety. |
| format | Article |
| id | doaj-art-230895cf5bb347cebcb315ccb1b0027d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-230895cf5bb347cebcb315ccb1b0027d2025-08-20T03:16:43ZengMDPI AGApplied Sciences2076-34172025-06-011513711510.3390/app15137115Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging TechnologiesLars Skaug0Mehrdad Nojoumian1Nolan Dang2Amy Yap3Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USADepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USADepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USADepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USATraffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety.https://www.mdpi.com/2076-3417/15/13/7115road safetytraffic crashescrash data analysisstatistical and machine learningmethodological challengesbig data analytics |
| spellingShingle | Lars Skaug Mehrdad Nojoumian Nolan Dang Amy Yap Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies Applied Sciences road safety traffic crashes crash data analysis statistical and machine learning methodological challenges big data analytics |
| title | Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies |
| title_full | Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies |
| title_fullStr | Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies |
| title_full_unstemmed | Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies |
| title_short | Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies |
| title_sort | road crash analysis and modeling a systematic review of methods data and emerging technologies |
| topic | road safety traffic crashes crash data analysis statistical and machine learning methodological challenges big data analytics |
| url | https://www.mdpi.com/2076-3417/15/13/7115 |
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