Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction

• URL: http://arxiv.org/abs/2010.14921v1
• Date: Tue, 27 Oct 2020 11:52:39 GMT
• Title: Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction
• Authors: Muhammad Umer, Saima Sadiq, Abid Ishaq, Saleem Ullah, Najia Saher, Hamza Ahmad Madni
• Abstract summary: Various machine learning models are being used for accident prediction. Random Forest as the best performing model with highest classification with 0.974 accuracy, 0.954 precision, 0.930 recall and 0.942 F-score.
• Score: 2.956978593944786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid increase of traffic volume on urban roads over time has changed the traffic scenario globally. It has also increased the ratio of road accidents that can be severe and fatal in the worst case. To improve traffic safety and its management on urban roads, there is a need for prediction of severity level of accidents. Various machine learning models are being used for accident prediction. In this study, tree based ensemble models (Random Forest, AdaBoost, Extra Tree, and Gradient Boosting) and ensemble of two statistical models (Logistic Regression Stochastic Gradient Descent) as voting classifiers are compared for prediction of road accident severity. Significant features that are strongly correlated with the accident severity are identified by Random Forest. Analysis proved Random Forest as the best performing model with highest classification results with 0.974 accuracy, 0.954 precision, 0.930 recall and 0.942 F-score using 20 most significant features as compared to other techniques classification of road accidents severity.

Related papers

• Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding [45.7444555195196]
  This work introduces a multi-stage, multimodal pipeline to pre-process videos of traffic accidents, encode them as scene graphs, and align this representation with vision and language modalities for accident classification. When trained on 4 classes, our method achieves a balanced accuracy score of 57.77% on an (unbalanced) subset of the popular Detection of Traffic Anomaly benchmark.
  arXiv  Detail & Related papers  (2024-07-08T13:15:11Z)
• Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
  We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports. We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes. Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
  arXiv  Detail & Related papers  (2024-06-16T03:10:16Z)
• Predicting Overtakes in Trucks Using CAN Data [51.28632782308621]
  We investigate the detection of truck overtakes from CAN data. Our analysis covers up to 10 seconds before the overtaking event. We observe that the prediction scores of the overtake class tend to increase as we approach the overtake trigger.
  arXiv  Detail & Related papers  (2024-04-08T17:58:22Z)
• Prediction of Crash Injury Severity in Florida's Interstate-95 [0.0]
  Traffic crashes on Florida's Interstate-95 from 2016 to 2021 were gathered. classification methods were used to estimate the severity of driver injuries. The Adaboost algorithm outperformed the others in terms of recall and AUC.
  arXiv  Detail & Related papers  (2023-12-16T18:42:39Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline [0.5221459608786241]
  We gathered accident duration, road conditions, and meteorological data from a database of traffic accidents to check the feasibility of a traffic accident duration pipeline. Our binary classification random forest model distinguished between short-term and long-term effects with an 83% accuracy rate. The SHAP value analysis identified weather conditions, wind chill and wind speed as the most influential factors in determining the duration of an accident.
  arXiv  Detail & Related papers  (2023-11-01T16:33:37Z)
• Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model [0.0]
  This study investigates the effectiveness of the Random Forest machine learning algorithm for predicting the severity of an accident. The model is trained on a dataset of accident records from a large metropolitan area and evaluated using various metrics. Results show that the Random Forest model is an effective tool for predicting accident severity with an accuracy of over 80%.
  arXiv  Detail & Related papers  (2023-10-09T16:33:44Z)
• Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms [0.0]
  The data used in this study were obtained for traffic crashes on all interstates across the state of Texas from a period of six years between 2014 and 2019. The output of the proposed severity classification approach includes three classes for fatal and severe injury (KA) crashes, non-severe and possible injury (BC) crashes, and property damage only (PDO) crashes. The results of Granger causality analysis identified the speed limit, surface and weather conditions, traffic volume, presence of workzones, workers in workzones, and high occupancy vehicle (HOV) lanes, as the most important factors affecting crash severity
  arXiv  Detail & Related papers  (2021-11-30T20:32:31Z)
• On the explainability of hospitalization prediction on a large COVID-19 patient dataset [45.82374977939355]
  We develop various AI models to predict hospitalization on a large (over 110$k$) cohort of COVID-19 positive-tested US patients. Despite high data unbalance, the models reach average precision 0.96-0.98 (0.75-0.85), recall 0.96-0.98 (0.74-0.85), and $F_score 0.97-0.98 (0.79-0.83) on the non-hospitalized (or hospitalized) class.
  arXiv  Detail & Related papers  (2021-10-28T10:23:38Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.