Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

中文翻译：

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边缘计算框架下基于CPSO / SSVM算法的城市交通流量预测模型

城市交通流量预测一直是智慧城市建设的重要领域。随着近年来边缘计算技术的发展，智能城市的网络边缘节点能够实时收集和处理各种类型的城市交通数据，这导致部署具有实时分析功能的智能交通预测技术成为可能。并及时在边缘反馈。鉴于城市交通流的强烈非线性特征，涉及多个动态和静态影响因素，以及大城市地区短期交通流预测的难度越来越大，提出了一种基于混沌粒子群算法的城市交通流预测模型。算法平滑支持向量机（CPSO / SSVM）。该预测模型建立了新的二阶平滑函数以达到更好的逼近和回归效果，并通过混沌粒子群优化进一步提高了平滑支持向量机算法的计算效率。仿真实验结果表明，该模型能够准确预测城市交通流量。