Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm DOI Creative Commons
Xing Wang, Yikun Su,

Zhizhe Zheng

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e35595 - e35595

Published: Aug. 1, 2024

Providing accurate prediction of the severity traffic collisions is vital to improve efficiency emergencies and reduce casualties, accordingly improving safety reducing congestion. However, issue both predictive accuracy model interpretability predicted outcomes has remained a persistent challenge. We propose Random Forest optimized by Meta-heuristic algorithm framework that integrates spatiotemporal characteristics crashes. Through analysis motor vehicle crash data on interstate highways within United States in 2020, we compared various ensemble models single-classification models. The results show (RF) Crown Porcupine Optimizer (CPO) best results, accuracy, recall, f1 score, precision can reach more than 90 %. found factors such as Temperature Weather are closely related Closely indicators were analyzed interpretatively using geographic information system (GIS) based characteristic importance ranking results. enables crashes discovers important leading with an explanation. study proposes some areas consideration should be given adding measures nighttime lighting devices fatigue driving alert ensure safe driving. It offers references for policymakers address management urban development issues.

Language: Английский

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations DOI
Daniel Molina, Javier Poyatos, Javier Del Ser

et al.

Cognitive Computation, Journal Year: 2020, Volume and Issue: 12(5), P. 897 - 939

Published: July 5, 2020

Language: Английский

Citations

41
Using enhanced Variational Modal Decomposition and Dung Beetle Optimization Algorithm optimization-kernel Extreme Learning Machine model to forecast short-term wind power DOI
Guodong You,

Z. Chang,

Xing-Yun Li

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 236, P. 110904 - 110904

Published: Aug. 1, 2024

Language: Английский

Citations

5
Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems DOI
Xiaowei Wang

Evolutionary Intelligence, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 20, 2024

Language: Английский

Citations

5
Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model DOI Creative Commons
Y.F. Hou, Xuefeng Deng, 裕章 宮下

et al.

AIP Advances, Journal Year: 2024, Volume and Issue: 14(6)

Published: June 1, 2024

Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), long short-term memory (LSTM) proposed in this paper. The first uses VMD to decompose time series into intrinsic functions different frequencies capture multi-scale characteristics data. Then, CPO used optimize LSTM adaptive parameters global search ability robustness model. Finally, decomposed component input network learn spatiotemporal dependence relationship long-term prediction. experimental results show that compared with traditional model, CPO-LSTM VMD-LSTM achieves better performance many evaluation indices effectively improves application can provide an effective tool fields meteorology as well new ideas related research.

Language: Английский

Citations

4
Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm DOI Creative Commons
Xing Wang, Yikun Su,

Zhizhe Zheng

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e35595 - e35595

Published: Aug. 1, 2024

Providing accurate prediction of the severity traffic collisions is vital to improve efficiency emergencies and reduce casualties, accordingly improving safety reducing congestion. However, issue both predictive accuracy model interpretability predicted outcomes has remained a persistent challenge. We propose Random Forest optimized by Meta-heuristic algorithm framework that integrates spatiotemporal characteristics crashes. Through analysis motor vehicle crash data on interstate highways within United States in 2020, we compared various ensemble models single-classification models. The results show (RF) Crown Porcupine Optimizer (CPO) best results, accuracy, recall, f1 score, precision can reach more than 90 %. found factors such as Temperature Weather are closely related Closely indicators were analyzed interpretatively using geographic information system (GIS) based characteristic importance ranking results. enables crashes discovers important leading with an explanation. study proposes some areas consideration should be given adding measures nighttime lighting devices fatigue driving alert ensure safe driving. It offers references for policymakers address management urban development issues.

Language: Английский

Citations

4