GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance
Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time mon...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/2/362 |
| _version_ | 1797395601898340352 |
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| author | Arshad Jamal Tahir Mahmood Muhamad Riaz Hassan M. Al-Ahmadi |
| author_facet | Arshad Jamal Tahir Mahmood Muhamad Riaz Hassan M. Al-Ahmadi |
| author_sort | Arshad Jamal |
| collection | DOAJ |
| description | Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals. |
| first_indexed | 2024-03-09T00:36:53Z |
| format | Article |
| id | doaj.art-d13ae452d17f46c4af87ce3ba35b3854 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T00:36:53Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-d13ae452d17f46c4af87ce3ba35b38542023-12-11T18:07:08ZengMDPI AGSymmetry2073-89942021-02-0113236210.3390/sym13020362GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety SurveillanceArshad Jamal0Tahir Mahmood1Muhamad Riaz2Hassan M. Al-Ahmadi3Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, KFUPM BOX 5055, 31261 Dhahran, Saudi ArabiaDepartment of Technology, School of Science and Technology, The Open University of Hong Kong, 30 Good Shepherd Street, Ho Man Tin, Kowloon, Hong KongDepartment of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, 31261 Dhahran, Saudi ArabiaDepartment of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, KFUPM BOX 5055, 31261 Dhahran, Saudi ArabiaStatistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.https://www.mdpi.com/2073-8994/13/2/362road safetycrash frequency modelingCOM–Poisson regressionCUSUMEWMAgeneralized linear models |
| spellingShingle | Arshad Jamal Tahir Mahmood Muhamad Riaz Hassan M. Al-Ahmadi GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance Symmetry road safety crash frequency modeling COM–Poisson regression CUSUM EWMA generalized linear models |
| title | GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance |
| title_full | GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance |
| title_fullStr | GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance |
| title_full_unstemmed | GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance |
| title_short | GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance |
| title_sort | glm based flexible monitoring methods an application to real time highway safety surveillance |
| topic | road safety crash frequency modeling COM–Poisson regression CUSUM EWMA generalized linear models |
| url | https://www.mdpi.com/2073-8994/13/2/362 |
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