<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines provide a helpful and suitable API that helps to create and tune classification and prediction models to decision-making concerning traffic accidents. Data scientists have recently focused on classification and prediction techniques for traffic accidents; data analytics techniques for feature extraction have also continued to evolve. Analysis of a huge volume of received data requires considerable processing time. Practically, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in traffic accident recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from traffic accidents. Problems with overclocking during the digital processing of traffic accidents have yet to be completely resolved. Our proposed model is based on advanced processing by the Spark MlLib core. We call on the real-time data streaming API on spark to continuously gather real-time data from multiple external data sources in the form of data streams. Secondly, the data streams are treated as unbound tables. After this, we call the random forest algorithm continuously to extract the feature parameters from a traffic accident. The use of this proposed method makes it possible to increase the speed factor on processors. Experiment results showed that the proposed method successfully extracts the accident features and achieves a seamless classification performance compared to other conventional traffic accident recognition algorithms. Finally, we share all detected accidents with details onto online applications with other users.</p>
</abstract>

This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines provide a helpful and suitable API that helps to create and tune classification and prediction models to decision-making concerning traffic accidents. Data scientists have recently focused on classification and prediction techniques for traffic accidents; data analytics techniques for feature extraction have also continued to evolve. Analysis of a huge volume of received data requires considerable processing time. Practically, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in traffic accident recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from traffic accidents. Problems with overclocking during the digital processing of traffic accidents have yet to be completely resolved. Our proposed model is based on advanced processing by the Spark MlLib core. We call on the real-time data streaming API on spark to continuously gather real-time data from multiple external data sources in the form of data streams. Secondly, the data streams are treated as unbound tables. After this, we call the random forest algorithm continuously to extract the feature parameters from a traffic accident. The use of this proposed method makes it possible to increase the speed factor on processors. Experiment results showed that the proposed method successfully extracts the accident features and achieves a seamless classification performance compared to other conventional traffic accident recognition algorithms. Finally, we share all detected accidents with details onto online applications with other users.

A Distributed Big Data Analytics Model for Traffic Accidents Classification and Recognition based on SparkMlLib Cores

Phd Student in Big Data analytics, traffic accidents, Artificial Intelligence, LISAC Laboratory, Department of Computer Sciences, Faculty of Science

Sidi Mohamed Ben Abdellah University of Fez

Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, Sidi Mohammed ben Abdellah University

Faculty of Sciences Dhar el Mahraz, LISAC laboratory

Mohamed V University, Faculty of Sciences

Intelligent Processing Systems & Security Team (IPSS) Computer Science Department

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines...

A Distributed Big Data Analytics Model for Traffic Accidents Classification and Recognition based on SparkMlLib Cores

Online veröffentlicht: 20. Okt. 2023

Seitenbereich: 62 - 71

Eingereicht: 21. Juni 2022

Akzeptiert: 02. Aug. 2022

DOI: https://doi.org/10.14313/jamris/4-2022/34

Schlüsselwörter
Big data, machine learning, traffic accident, severity prediction, convolutional neural network

© 2022 Imad El Mallahi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Distributed Big Data Analytics Model for Traffic Accidents Classification and Recognition based on SparkMlLib Cores

Online veröffentlicht: 20. Okt. 2023

Seitenbereich: 62 - 71

Eingereicht: 21. Juni 2022

Akzeptiert: 02. Aug. 2022

DOI: https://doi.org/10.14313/jamris/4-2022/34

SchlüsselwörterBig data, machine learning, traffic accident, severity prediction, convolutional neural network

© 2022 Imad El Mallahi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Big data, machine learning, traffic accident, severity prediction, convolutional neural network