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However, the performance of the systems offering 5G\u2010based services depends on various factors. In this paper, we consider the case of the online railway ticketing system in China that serves the needs of hundreds of millions of people daily. This system\u2019s online access rates vary over time, and fluctuations are experienced, affecting its overall dependability and service quality. We use long short\u2010term memory network, particle swarm optimization, and differential evolution to construct DP\u2010LSTM\u2014a hybridly optimized model to predict network flow for dependable and quality\u2010enhanced service delivery. We evaluate the proposed model using real data collected over six months from the \u201c12306 online ticketing\u201d system. We compare the performance of the proposed model with mainstream network traffic prediction models. We use mean absolute percentage error, mean absolute error, and root mean square error for performance evaluation. 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