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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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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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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DOI: https://doi.org/10.1007/978-3-030-02686-8_23
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