Abstract
The missing information issue stays as a trouble in transportation data framework, which genuinely confined the utilization of insightful transportation framework, for example, movement control and activity stream forecast. To take care of this issue, various ascription techniques had been proposed in the most recent decade. Notwithstanding, few existing reviews had completely utilized the spatial relationship of movement information ascription. Street mishap casualty rate relies on many elements, and it is an exceptionally difficult undertaking to examine the conditions between the qualities in view of the numerous natural and street mischance variables. Any missing information in the database could darken the revelation of essential variables and prompt to invalid conclusions. We propose a new method for missing data imputation named genetic algorithm with classification. Our results indicate that the proposed method performs significantly better than the existing algorithms.
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Midde, R., K. G, S., B, E.R. (2018). Missing Values Imputation Using Genetic Algorithm for the Analysis of Traffic Data. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_25
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DOI: https://doi.org/10.1007/978-981-10-7868-2_25
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