Crash Data Analysis and Network Screening using Data Mining by the Deep Convolutional Neural Network (DCNN)

Crash Data Analysis and Network Screening using Data Mining by the Deep Convolutional Neural Network (DCNN) | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 February 2025 V1 Latest version Share on Crash Data Analysis and Network Screening using Data Mining by the Deep Convolutional Neural Network (DCNN) Authors : Mohammadreza Pilehchi , Hamid Reza Behnood , and Hamid Mirzahossein 0000-0003-1615-9553 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174048783.32475259/v1 251 views 149 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Urban intersections, critical hotspots for severe traffic crashes, demand advanced analytical approaches, particularly in developing nations where such methods remain underexplored. This study introduces a novel data mining framework integrating deep learning to analyze collision patterns at urban intersections in Qazvin, Iran. Utilizing 245 crash records (2021–2023), the research applied k-means clustering to categorize variables, regression trees for classification, and a deep convolutional neural network (DCNN) to evaluate data clusters. Three primary crash-influencing factors emerged: vehicle type, human factors, and lighting conditions. Training the DCNN on 2021–2022 data and testing it on 2023 data yielded 97% accuracy in predicting crash determinants. Findings highlighted unique crash characteristics across intersections, emphasizing context-specific risk factors. The model enables precise identification of variable importance, offering actionable insights for targeted safety interventions. This approach bridges methodological gaps in crash analysis for developing regions, demonstrating the efficacy of hybrid data mining and deep learning in enhancing intersection safety planning. By prioritizing key risk variables and enabling predictive analytics, the framework supports data-driven policymaking to mitigate crash severity and frequency in urban settings. Supplementary Material File (manuscript (1).docx) Download 2.34 MB Information & Authors Information Version history V1 Version 1 25 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords traffic engineering computing transportation transportation engineering Authors Affiliations Mohammadreza Pilehchi Imam Khomeini International University View all articles by this author Hamid Reza Behnood Imam Khomeini International University View all articles by this author Hamid Mirzahossein 0000-0003-1615-9553 [email protected] Imam Khomeini International University View all articles by this author Metrics & Citations Metrics Article Usage 251 views 149 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammadreza Pilehchi, Hamid Reza Behnood, Hamid Mirzahossein. Crash Data Analysis and Network Screening using Data Mining by the Deep Convolutional Neural Network (DCNN). Authorea . 25 February 2025. DOI: https://doi.org/10.22541/au.174048783.32475259/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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