Machine Learning Approach to Predict Traffic Accident Occurrence in Bangladesh

Ahsan, Annesha; Moon, Nazmun Nessa; Sharmin, Shayla; Islam, Mohammad Monirul; Hossain, Refath Ara; Nawshin, Samia

doi:10.1109/becithcon54710.2021.9893578

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…XGBoost and SHAP methods [11] Enhanced model accuracy and transparency, explaining spatial variations May require substantial computational resources 2D hotspot analysis and machine learning [15] Improved prediction accuracy and robustness, fine-grained spatial partitioning Complex classification models may require large datasets Decision trees and random forest [16] Effective prediction of road traffic accidents, multiple input attributes More feature engineering and data preprocessing may be needed Random forest algorithm [17] Revealing correlations between different factors, providing fire prevention methods Data quality and correlations may affect prediction accuracy…”

Section: Requires An Effective Data Clustering Methodsmentioning

confidence: 99%

“…Additionally, in certain instances, different types of security events may exhibit evident or covert interactions. For instance, Ahsan et al [16] proposed a machine learning-based approach to analyze and predict road traffic accident risks. They selected multiple attributes as input variables and utilized decision trees and random forest algorithms to predict accidents.…”

Section: Crime Event Predictionmentioning

confidence: 99%

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Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Wang,

Tian,

Pan

2023

Electronics

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Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates.

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Section: Requires An Effective Data Clustering Methodsmentioning

confidence: 99%

Section: Crime Event Predictionmentioning

confidence: 99%

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Wang,

Tian,

Pan

2023

Electronics

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“…Bayesian logistic regression model was used by Xu, et al [8] using four different congestion levels as dependent variable. Nevertheless, statistical approaches often come with significant assumptions related to data distribution and preparation [9][10][11][12][13][14]. These prerequisites can often impair the accuracy of crash likelihood predictions.…”

Section: A Models In Crash Likelihood Predictionmentioning

confidence: 99%

Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data

Islam,

Abdel-Aty,

Anik

2023

IEEE Open J. Intell. Transp. Syst.

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Crash prediction is one of the important elements of real time traffic management strategies. Previous studies have demonstrated the use of infrastructure-based detector data and UAV video to predict a crash in the near future. The main limitation of such data is limited coverage. In this work, we have used connected vehicle trajectory data that can have wide coverage as well as provide insight into the trajectory that might lead to a crash. The trajectory data was provided by Wejo which collects data from the manufacturer and was spaced at 3 seconds. GPS locations and their associated time series features such as speed, acceleration and yaw rate were used to feed into an ensembled Transformer and Conformer model. A voting classifier was used to obtain the output of the final model which achieved a recall of 76% and the false alarm rate of 30%. This study showed how connected vehicle trajectory data can aid in getting insight into crashes. While most previous studies focus on using aggregated data to estimate crashes, the proposed work shows that trajectory data mining can also provide competitive results.

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