山东理工大学学报（自然科学版）2025，Vol.39Issue(4)：9-15,7.
基于BP神经网络与GIS的出行方式识别
Travel mode identification based on BP neural network and GIS
摘要
Abstract
The planning and construction of modern urban transportation heavily rely on extensive and de-tailed survey data.Existing questionnaire surveys and travel log surveys are cumbersome,have long data update cycles,low efficiency,and high costs.This study uses GPS to collect travel trajectory data and combines a BP neural network model with GIS map matching to identify five travel modes:walking,cyc-ling,electric vehicles,buses,and taxis.The accuracy of the proposed algorithm is validated through the confusion matrix,and the results show that the recognition accuracy for all five travel modes exceed 95%.The findings provide valuable insights for improving transportation survey methods.关键词
出行方式/识别/BP神经网络/GIS/GPSKey words
trip mode/identify/BP neural network/GIS/GPS分类
交通工程引用本文复制引用
王杰,杨坤,张文斌..基于BP神经网络与GIS的出行方式识别[J].山东理工大学学报（自然科学版）,2025,39(4):9-15,7.基金项目
山东省重点研发计划项目(2018GGX105010) （2018GGX105010）
