Urban Crowd Flow Forecasting Based on Cellular Network

摘要：

　　Forecasting the crowd flows in a city is crucial for public safety,traffic management and urban planning.Researchers proposed several methods to forecast the crowd flows.However,they omitted the acquisition of comprehensive flow data.Taxis'GPS trajectory data and bike sharing system data are often used in these works as the flow data.But they are not able to reflect the comprehensive crowd flows in a city,since they only contain the transitions of a specific transportation mode.In this paper,we propose to extract comprehensive crowd flows from mobile flow records(MFRs),a fine-grained cellular data.We also use a Convolution Neural Network(CNN)based method to forecast crowd flows and compare it with traditional time series regression models.The experiments on a large-scale cellular dataset show that CNN based method can reduce the error by 28%to 77%.

关键词： crowd flow forecasting cellular network DNN

作者: Yi Zhao Jianbo Li Xin Miao Xuan Ding

作者单位: School of Software,Tsinghua University Computer Science and Technology College,Qingdao University

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 427-431

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）