【摘要】 交通流信息预测是智能交通系统进行交通疏导管理的重要基础，为城市交通管理规划提供可靠的数据支持和科学的决策依据。由于交通流量数据是实时更新的增量流数据，每次更新历史数据集时都需要重新构建预测模型，消耗了大量计算资源和运行时间，为此提出一种基于改进在线顺序极限学习机的交通流预测模型(IOS-ELM),通过构建新增数据的增强特征映射关系，生成交通流动态更新特征表示空间，实现短时交通流预测模型的动态更新。利用长沙市远大一路交通流数据评估该模型，实验结果表明，IOS-ELM模型在NRMSE和MAPE的预测性能上均超过其他基准预测模型(MLP、ELM、OS-ELM和SVR),同时模型的预测耗时较小，可以保证一定实时性，满足城市道路交通流的实时准确预测的需求。更多还原

【Abstract】 Traffic flow information prediction is an important foundation for traffic guidance management of intelligent transportation systems, and provides reliable data support and scientific decision-making basis for urban traffic management planning. Since the traffic flow data is real-time updated incremental flow data, each time the historical data set is updated, the prediction model needs to be rebuilt, which consumes a lot of computing resources and running time. Therefore, this paper proposes an improved online sequential extreme learning machine for traffic flow prediction(IOS-ELM), which constructs the enhanced feature mapping relationship of the newly input data, generates the dynamic update feature representation space of the traffic flow, and realizes the dynamic update of the short-term traffic flow prediction model. Finally, the model is evaluated on the real-world traffic flow data of Yuanda 1 st Road in Changsha, China. The experimental results show that the IOS-ELM model exceeds other baselines prediction models(MLP, ANN, ELM, OS-ELM) in the prediction performance of NRMSE and MAPE. Meanwhile, the computation prediction of IOS-ELM is less time-consuming, which can ensure a certain real-time performance and meet the needs of real-time and accurate prediction of urban road traffic flow.更多还原