Abstract
Advances in the use of an intelligent transportation system (ITS) have been deployed in most of the world, which presents new opportunities for develo** sustainable transportation system. This paper focuses on improving intelligent transportation systems using Big Data tools in predicting road accidents in Nigeria, based on real-time data gotten from Twitter. The work gives a review of common problems associated with the intelligent transportation system, and how this can be improved by utilizing Apache Spark Big Data. The revolution in intelligent transportation systems can be impacted by the availability of large data that can be used to generate new functions and services in intelligent transportation systems. The framework for utilizing Big Data Apache Spark will be discussed. The Big Data Apache Spark applications will be used to collect a large amount of data from various sources in intelligent transportation system; in return, this will help in predicting road accidents before it happens, and also, a feedback system for alerting can be projected. The use of machine learning algorithms is being used to make necessary predictions for the intelligent transportation system. The result obtained shows that for the classified data relating to road accidents, KNN gave a 94% accuracy when compared to other classification algorithms such as the Naïve Bayes, support vector machine, and the decision tree.
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This paper was sponsored by Covenant University, Ota, Ogun State Nigeria.
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Ademola, O.F., Misra, S., Agrawal, A. (2023). Improving Real-Time Intelligent Transportation Systems in Predicting Road Accident. In: Singh, Y., Verma, C., Zoltán, I., Chhabra, J.K., Singh, P.K. (eds) Proceedings of International Conference on Recent Innovations in Computing. ICRIC 2022. Lecture Notes in Electrical Engineering, vol 1011. Springer, Singapore. https://doi.org/10.1007/978-981-99-0601-7_18
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