Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review-Reference-Cited by-同舟云学术

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review

Published:2024-01 Issue: Volume:194 Page:107378
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Ali Yasir^ORCID,Hussain Fizza,Haque Md Mazharul

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety, Risk, Reliability and Quality,Human Factors and Ergonomics

Reference132 articles.

1. Investigating the nature and impact of reporting bias in road crash data;Abay;Transp. Res. A Policy Pract.,2015

2. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections;Abdelwahab;Transp. Res. Rec.,2001

3. A proactive decision support system for predicting traffic crash events: a critical analysis of imbalanced class distribution;Abou Elassad;Knowl.-Based Syst.,2020

4. A real-time crash prediction fusion framework: an imbalance-aware strategy for collision avoidance systems;Abou Elassad;Transp. Res. Part C: Emerg. Tech.,2020

5. Heterogeneous ensemble learning for enhanced crash forecasts–a frequentist and machine learning based stacking framework;Ahmad;J. Saf. Res.,2022

Cited by 20 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. Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework;Accident Analysis & Prevention;2024-11

2. Analyzing urban traffic crash patterns through spatio-temporal data: A city-level study using a sparse non-negative matrix factorization model with spatial constraints approach;Applied Geography;2024-11

3. Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling;Accident Analysis & Prevention;2024-11

4. A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability;Accident Analysis & Prevention;2024-10

5. Machine learning for predictions of road traffic accidents and spatial network analysis for safe routing on accident and congestion-prone road networks;Results in Engineering;2024-09