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Prediction of Traffic-Violation Using Data Mining Techniques

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Proceedings of the Future Technologies Conference (FTC) 2018 (FTC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 880))

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Abstract

This paper presents the prediction of traffic-violations using data mining techniques, more specifically, when most likely a traffic-violation may happen. Also, the contributing factors that may cause more damages (e.g., personal injury, property damage, etc.) are discussed in this paper. The national database for traffic-violation was considered for the mining and analyzed results indicated that a few specific times are probable for traffic-violations. Moreover, most accidents happened on specific days and times. The findings of this work could help prevent some traffic-violations or reduce the chance of occurrence. These results can be used to increase cautions and traffic-safety tips.

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Notes

  1. 1.

    https://www.cs.waikato.ac.nz/~ml/weka/downloading.html.

  2. 2.

    https://catalog.data.gov/dataset.

  3. 3.

    http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm.

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Acknowledgment

The author would like to thank to open data website (https://catalog.data.gov/dataset) for making the dataset available for research and analysis. A special thank you to those who participated in the initial presentation and provided valuable feedback (part of this paper was presented and was submitted as a class project). Also, thank to Dr. Kambiz Ghazinour for helping me to think further about the data and analysis process.

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Authors and Affiliations

  1. Kent State University, Kent, OH, 44242, USA

    Md Amiruzzaman

Authors
  1. Md Amiruzzaman
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Correspondence to Md Amiruzzaman .

Editor information

Editors and Affiliations

  1. Saga University , Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

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Amiruzzaman, M. (2019). Prediction of Traffic-Violation Using Data Mining Techniques. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_23

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