计算机工程与应用2023，Vol.59Issue(24)：360-366,7.DOI:10.3778/j.issn.1002-8331.2210-0046
采用多任务学习预测短时公交客流
Predict Short Term Bus Passenger Flow via Multi-Task Learning
摘要
Abstract
Most of the existing methods for short-term bus passenger flow prediction only rely on the information of a sin-gle bus line,which may ignore the impact of the correlation of multiple lines on the improvement of prediction accuracy.Thus,a prediction method based on correlation analysis and multi-task learning is proposed.Firstly,the correlation coeffi-cient of multiple bus lines is obtained by using grey correlation analysis and Pearson correlation coefficient.Then,taking the passenger flow prediction of relevant bus lines as the auxiliary task of the passenger flow prediction of the current line,a multi-task deep learning model based on gate recurrent unit(GRU)neural network is established to predict the pas-senger flow.Experimental results show that the multi-task deep learning model performs better on the prediction accuracy compared against the traditional time series prediction model and the neural network model that considers a single bus line only.关键词
公交短时客流预测/门控循环单元(GRU)神经网络/多任务学习/灰色关联分析Key words
short-term bus passenger flow forecasting/gate recurrent unit(GRU)neural network/multi-task learning/grey correlation analysis分类
信息技术与安全科学引用本文复制引用
张鹏祯,左兴权,黄海..采用多任务学习预测短时公交客流[J].计算机工程与应用,2023,59(24):360-366,7.基金项目
国家自然科学基金(61873040). （61873040）
