Applications of machine learning methods in traffic crash severity modelling: current status and future directions-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Applications of machine learning methods in traffic crash severity modelling: current status and future directions

Published:2021-07-20 Issue:6 Volume:41 Page:855-879
ISSN:0144-1647
Container-title:Transport Reviews
language:en
Short-container-title:Transport Reviews

Author:

Wen Xiao¹,Xie Yuanchang¹,Jiang Liming¹,Pu Ziyuan²,Ge Tingjian³

Affiliation:

1. Department of Civil and Environmental Engineering, University of Massachusetts Lowell, Lowell, MA, USA

2. School of Engineering, Monash University, Bandar Sunway, Malaysia

3. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, USA

Publisher

Informa UK Limited

Subject

Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/01441647.2021.1954108

Reference99 articles.

1. Investigating the nature and impact of reporting bias in road crash data

2. Predicting Injury Severity Levels in Traffic Crashes: A Modeling Comparison

3. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections

4. Artificial Neural Networks and Logit Models for Traffic Safety Analysis of Toll Plazas

5. Analysis of traffic accident severity using Decision Rules via Decision Trees

Cited by 46 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. Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach;Accident Analysis & Prevention;2024-11

2. Effect of spatial relationship between curves on crash severity at horizontal curves in a mountainous terrain;Accident Analysis & Prevention;2024-10

3. Optimizing crash risk models for freeway segments: A focus on the heterogeneous effects of road geometric design features, traffic operation status, and crash units;Accident Analysis & Prevention;2024-09

4. Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm;Heliyon;2024-08

5. Estimating the frequency of traffic overloading on road bridges;Journal of Traffic and Transportation Engineering (English Edition);2024-08

同舟云学术

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