ActivityNET:Neural networks to predict public transport trip purposes from individual smart card data and POIs  

作　　者：Nilufer Sari Aslam Mohamed R.Ibrahim Tao Cheng Huanfa Chen Yang Zhang 

机构地区：[1]SpaceTimeLab,Department of Civil,Environmental and Geomatic Engineering,University College London(UCL),London,UK [2]Centre for Advanced Spatial Analysis(CASA),University College London,London,UK

出　　处：《Geo-Spatial Information Science》2021年第4期711-721,共11页地球空间信息科学学报（英文）

基　　金：This work is part of the Consumer Data Research Centre project(ES/L011840/1);funded by the UK Economic and Social Research Council(grant number 1477365).

摘　　要：Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

关 键 词：Trip purpose prediction smart card data POIs neural networks machine learning 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]