Taxi drivers’ traffic violations detection using random forest algorithm: A case study in China

Wan, Ming; Wu, Qian; Yan, Lixin; Guo, Junhua; Li, Wenxia; Lin, Wei; Lu, Shan

doi:10.1080/15389588.2023.2191286

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…It also had the lowest score on the F1 scale. Consequently, this was represented in the considerably more significant misclassifications apparent in the confusion matrix shown in Fig 9 . On the other hand, Random Forests provide additional benefits for crash investigation because of their interpretability and minimum tuning requirements [35,36].…”

Section: Random Forest Modelmentioning

confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

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Section: Random Forest Modelmentioning

confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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“…Furthermore, RF offers crucial benefits, such as relative variable importance and partial dependency plots, making the interpretation of RF results effortless. RF is widely employed for classification and regression tasks, as illustrated by Breiman (1996) [55] and Wan et al (2023) [56].…”

Section: Random Forestmentioning

confidence: 99%

Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms

Ulu,

Kilic,

Türkan

2024

Applied Sciences

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This paper presents a novel geohash-based approach for predicting traffic incident locations using machine learning algorithms. The study utilized a three-stage model for predicting the locations of traffic incidents, which encompassed accidents, breakdowns, and other incidents. In the model, firstly, ArcGIS was used to convert the coordinates of traffic incidents into geohash areas, leading to the definition of incident locations. Secondly, variables affecting traffic incidents were extracted, and a dataset was created by utilizing the values of these variables in geohash fields. Finally, machine learning algorithms such as decision tree (DT), k-nearest neighbor (k-NN), random forest (RF), and support vector machine (SVM) algorithms were used to predict the geohash region of traffic incidents. After conducting hyperparameter optimization, we evaluated the efficacy of various machine learning algorithms in predicting the location of traffic incidents using different evaluation metrics. Our findings indicate that the RF, SVM, and DT models performed the best, with accuracy percentages of 91%, 88%, and 87%, respectively. The findings of the research revealed that traffic incident locations can be successfully predicted with the geohash-based forecasting model. The results offer traffic managers and emergency responders new perspectives on how to manage traffic incidents more effectively and improve drivers’ safety.

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Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework

Sun,

Ai,

Wang

et al. 2024

Accident Analysis & Prevention

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Taxi drivers’ traffic violations detection using random forest algorithm: A case study in China

Cited by 4 publications

References 39 publications

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework

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