Predicting pedestrian-involved crash severity using inception-v3 deep learning model
Author:
Publisher
Elsevier BV
Reference60 articles.
1. Abdelwahab, H.T., Abdel-Aty, M.A., 2001. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections: 10.3141/1746-02 1746 , 6–13. 10.3141/1746-02.
2. A comprehensive study of child pedestrian crash outcomes in Ghana;Adanu;Accid. Anal. Prev.,2023
3. Gully erosion susceptibility assessment in the kondoran watershed using machine learning algorithms and the boruta feature selection;Ahmadpour;Sustainability (switzerland),2021
4. Severity Prediction of Traffic Accident Using an Artificial Neural Network;Alkheder;J. Forecast.,2017
5. The urban structure and pedestrian injuries: A typological analysis of pedestrian crashes in the city of Hermosillo;Armenta-Ramirez;Mexico. Traffic Injury Prevention,2023
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention;Engineering Applications of Artificial Intelligence;2024-09
2. Unconstrained and partially constrained temporal modelling of pedestrian injury severities;Transportmetrica A: Transport Science;2024-08-08
3. A Comparative Study Using Generalized Ordered Probit, Stacking Ensemble, and TabNet: Application to Determinants of Pedestrian Crash Severity;Data Science for Transportation;2024-06-28
4. Deep Learning Used for Recognition of Landmark;2024 4th International Conference on Data Engineering and Communication Systems (ICDECS);2024-03-22
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3