交通信息与安全2024，Vol.42Issue(2)：158-165,8.DOI:10.3963/j.jssn.1674-4861.2024.02.016
基于异构数据特征的城市轨道交通OD客流短时预测方法
A Short-term Prediction for OD Passenger Flow in Urban Rail Transit Based on Heterogeneous Data Feature Extraction
摘要
Abstract
As an important basis for rail transit operations and travel choices,prediction for origin-destination(OD)passenger flow in urban rail transit is of great significance in intelligent transportation systems.The conventional convolutional neural network(CNN)mostly focuses on local OD features due to their translation invariance and lo-cal sensitivity.To improve its global perception capacity in OD matrix modeling,a heterogeneous data feature ex-traction machine(HDFEM)model is proposed based on attention mechanism.The model constructs a heteroge-neous data OD spatio-temporal tensor and a geographic information tensor from the perspective of spatio-temporal characteristics and land use attributes.It segments and encodes heterogeneous data tensors via a tensor coding layer to obtain the features of tensor blocks in heterogeneous data tensors.Then,it connects the features of each tensor block through the attention mechanism to extract the spatial correlation among various OD matrix parts.This ap-proach not only realizes multi-source heterogeneous data fusion,but also extracts remote features of OD matrix.Meanwhile,the model uses long short-term memory(LSTM)network to deal with the OD temporal feature.Com-pared with the convolutional neural network-based prediction model,the results on the Hangzhou metro auto fare collection(AFC)dataset show that the mean square error,mean absolute error,and normalized root mean square er-ror of the HDFEM model decreases by 4.1%,2.5%,and 2%,respectively.The importance of extracting whole spa-tial features for OD passenger flow prediction of urban rail transit is verified.关键词
智能交通/OD客流预测/异构数据融合模型/深度学习/注意力机制/城市轨道交通Key words
intelligent transportation/OD passenger flow prediction/heterogeneous data fusion model/deep learn-ing/attention mechanism/urban rail transit分类
交通工程引用本文复制引用
陈喜群,沈楼涛,李俊懿,李传家..基于异构数据特征的城市轨道交通OD客流短时预测方法[J].交通信息与安全,2024,42(2):158-165,8.基金项目
国家自然科学基金项目(72171210)资助 （72171210）
