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Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type\u2010specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E\u2010IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E\u2010IDC framework achieved performance gains of 5%\u201356% in terms of incident severity classification and 1%\u201314% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E\u2010IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.<\/jats:p>","DOI":"10.1155\/2021\/5543698","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T17:35:50Z","timestamp":1618076150000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7829-7647","authenticated-orcid":false,"given":"Zafar","family":"Iqbal","sequence":"first","affiliation":[]},{"given":"Majid I.","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Shahid","family":"Hussain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-5347","authenticated-orcid":false,"given":"Asad","family":"Habib","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"volume-title":"Traffic Incident Management Handbook","year":"2010","author":"Owens N.","key":"e_1_2_11_1_2"},{"key":"e_1_2_11_2_2","first-page":"61","article-title":"A study of drivers\u2019 behaviors using a driving simulator with special reference of driving anger and traffic congestion","volume":"23","author":"Song H. 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