高技术通讯2025，Vol.35Issue(12)：1300-1310,11.DOI:10.3772/j.issn.1002-0470.2025.12.004
地震数据驱动的城市出行智能评估模型
Intelligent urban travel evaluation model driven by seismic data
摘要
Abstract
Urban travel intensity serves as a critical indicator of human activity,offering valuable insights for forecasting trends and understanding the impact of anthropogenic activities on both the environment and society.However,challenges in collecting comprehensive,accurate travel data and ensuring privacy have made it difficult to accurate-ly estimate urban travel intensity.In this study,an innovative approach by leveraging continuous seismic data is proposed to quantify urban travel intensity,thus effectively addressing issues of large-scale data collection and pri-vacy concerns.We develop a machine learning framework that integrates gradient boosting trees and long short-term memory(LSTM)networks to extract features related to travel intensity,overcoming the complexity and variability of seismic data.The experimental results demonstrate high training accuracy in Hubei,Hebei,and Shanxi prov-inces,and strong generalization performance across eight cities in different regions.The model's predictions suc-cessfully capture the significant changes in travel intensity patterns during various stages of the COVID-19 pandem-ic.This study offers an objective,low-cost,and privacy-preserving method for assessing urban travel intensity using seismic data,providing scientific support for sustainable urban planning and policy development.关键词
城市出行强度/梯度提升树/地震数据/长短期记忆Key words
urban travel intensity/gradient boosting tree/seismic data/long short-term memory引用本文复制引用
郭凯,黎建辉,师亮..地震数据驱动的城市出行智能评估模型[J].高技术通讯,2025,35(12):1300-1310,11.基金项目
国家重点研发计划(2021YFE0111500),中国科学院国际大科学计划培育专项(241711KYSB20200023)和山西省重点研发计划(202202020101009)资助项目. （2021YFE0111500）
