交通节能与环保2026，Vol.22Issue(3)：122-126,5.DOI:10.3969/j.issn.1673-6478.2026.03.024
基于多源数据融合和BP神经网络的公交客流预测研究
Research on Public Transport Passenger Flow Prediction Based on Multi source Data Fusion and BP Neural Network
王帅杰 1边天宇2
作者信息
- 1. 山东交通学院,山东 济南 250000
- 2. 中国建筑土木建设有限公司,山东 泰安 271000
- 折叠
摘要
Abstract
This study aims to explore the effective integration and application of multi-source data(including IC card data,passenger flow meters data,etc.)in the context of new urban ground public transportation travel patterns and prediction methods for urban ground public transportation passenger flow prediction.The core objective of the study is to address the issues of integrating and utilizing multi-source data,as well as the specific application and optimization of BP neural networks in urban public transportation passenger flow prediction.To this end,this paper systematically elaborates on the types and interrelationships of urban ground public transportation multi-source data,combines bus IC card data and passenger flow meter data,and uses Matlab and multi-source data fusion technology to systematically construct and optimize the BP neural network.Through the comparative analysis of the predicted results and actual values,it is found that the model has a good fitting degree and has achieved substantive progress in urban ground public transportation passenger flow prediction.关键词
多源数据/城市地面公交/客流预测/BP神经网络/MatlabKey words
multi source data/urban ground buses/passenger flow prediction/BP neural network/matlab分类
交通工程引用本文复制引用
王帅杰,边天宇..基于多源数据融合和BP神经网络的公交客流预测研究[J].交通节能与环保,2026,22(3):122-126,5.
