Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining
This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6562649 |
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| author | Yansong Qu Zhenlong Li Qin Liu Mengniu Pan Zihao Zhang |
| author_facet | Yansong Qu Zhenlong Li Qin Liu Mengniu Pan Zihao Zhang |
| author_sort | Yansong Qu |
| collection | DOAJ |
| description | This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes. |
| format | Article |
| id | doaj-art-8f7d0912cb804808a0dabf8271b0f6a4 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8f7d0912cb804808a0dabf8271b0f6a42025-02-03T01:01:21ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6562649Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule MiningYansong Qu0Zhenlong Li1Qin Liu2Mengniu Pan3Zihao Zhang4The College of Metropolitan TransportationThe College of Metropolitan TransportationThe College of Metropolitan TransportationThe College of Metropolitan TransportationThe College of Metropolitan TransportationThis study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.http://dx.doi.org/10.1155/2022/6562649 |
| spellingShingle | Yansong Qu Zhenlong Li Qin Liu Mengniu Pan Zihao Zhang Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining Journal of Advanced Transportation |
| title | Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining |
| title_full | Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining |
| title_fullStr | Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining |
| title_full_unstemmed | Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining |
| title_short | Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining |
| title_sort | crash near crash analysis of naturalistic driving data using association rule mining |
| url | http://dx.doi.org/10.1155/2022/6562649 |
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