A Traffic Event Detection Method Based on Random Forest and Permutation Importance

Su, Ziyi; Liu, Qingchao; Zhao, Chunxia; Sun, Fengming

doi:10.3390/math10060873

Cited by 8 publications

(4 citation statements)

References 20 publications

(23 reference statements)

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“…Gatera [31] developed two models using training and validation datasets to predict traffic accidents using RF. Su et al [32] used an RF model to study and evaluate the importance of five continuous variables (average speed, queue length, cumulative number of vehicles in queue, cumulative duration, and cumulative number of vehicles) on traffic flow variables. Yan and Shen [33] used RF as their basic prediction model, and tuned its parameters using Bayesian Optimization (BO), and analyzed the severity of traffic accidents with 15 factors related to traffic, time, and weather.…”

Section: Random Forestmentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

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Section: Random Forestmentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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“…The integration of permutation and leave-one-out importance methods provides a robust sensitivity analysis framework for understanding the model’s feature importance. More detailed explanations can be found in these studies [ 70 , 71 , 72 ].…”

Section: Employed ML Modelsmentioning

confidence: 99%

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Ahmed,

Song,

Zhang

et al. 2023

Materials

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Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model’s predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R2 = 0.96 for training and R2 = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R2 = 0.96 for train and R2 = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens.

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“…Li et al [7] used a mixed decision tree model to predict the delay of emergency vehicles. Su et al [8] judged traffic events based on random forest and permutation importance method. Bi et al [9] used the XGBoost model to judge the electronic bills, and finally compared with traditional machine learning algorithms such as decision trees and random forests, which fully prove its superiority.…”

Section: Introductionmentioning

confidence: 99%

Research on precise management and control technology of construction waste based on optimized PSO-ELM

GAO,

PARK

2024

ACE

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The turbulence in the transportation of the muck truck and the aging of the sensor, make the data from muck truck sensors contain a lot of noise points. This has a serious impact on the management of construction waste which can lead to This has a serious impact on the management of construction waste which can lead to a great deal of time and money wasted by the regulators and transport drivers. To solve this problem, this paper firstly analyses the fault diagnosis results of vehicle sensors. Based on it, this paper then uses the fuzzy clustering method to creatively build a fault credit system of the muck truck. This fault credit system analyses the past performance of truck sensors and presents the results in a form of reliability. Combining the data with the reliability of the sensors is beneficial to reduce the influence of noise on the discrimination of electronic bills. Finally in the pattern recognition section, this paper improves PSO-ELM method to make the fault credit system of the muck truck can adjust the weight matrix and the offset matrix in the neural network. Therefore, the credit system can directly adjust the result of the electronic single discrimination without wasting extra computing power. The effectiveness and superiority of the method is verified in the dataset collected from real truck.

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A Traffic Event Detection Method Based on Random Forest and Permutation Importance

Cited by 8 publications

References 20 publications

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Research on precise management and control technology of construction waste based on optimized PSO-ELM

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