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Summary In the face of the current huge amount of intelligent traffic data, collecting and statistical processing is a necessary and important process. But the inevitable data missing problem is the current research focus. In this paper, a novel approach of tensor‐based data missing estimation for Internet of Vehicles is proposed for the problem of missing the Internet of Vehicles data: Integrated Bayesian tensor decomposition (IBTD). In the data model construction stage, the random sampling principle is used to randomly extract the missing data to generate a subset of data. And the optimized Bayesian tensor decomposition algorithm is used for interpolation. Introduce the integration idea, analyze, and sort the error results after multiple interpolations, consider the space‐time complexity, and choose the optimal average to get the best result. The performance of the proposed model was evaluated by mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results show that the proposed method can effectively interpolate the traffic data sets with different missing quantities and get good interpolation results.

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Abstract. This paper puts forward a novel algorithm called extreme learning machine (ELM)which is optimized by improved genetic algorithm(IGA), and points out the weaknesses of ELM. The input weights and thresholds randomly generated by ELM are optimized by IGA. After it, ELM can get the more effective input weights and thresholds and be better applied in function approximation. The results of simulation shows that the optimized algorithm has a high approximation accuracy and faster convergence speed.

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