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A high-precision heuristic model to detect home and work locations from smart card data

Geo-Spatial Information Science 2019年第1期22卷 1-11页

作者： nilufer sari aslam Tao Cheng James Cheshire SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondonUK Department of Geography University College LondonLondonUK

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of *** and analyzing these data open new opportunities in urban modeling and travel behavior *** study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work *** model uses journey counts as an indicator of usage regularity,visit-frequency to identify activity locations for regular commuters,and stay-time for the classification of work and home locations and *** is taken as a case study,and the model results were validated against survey data from the London Travel Demand Survey and volunteer *** demonstrate that the proposed model is able to detect meaningful home and work places with high *** study offers a new and cost-effective approach to travel behavior and demand research.

关键词： Smart card data activity location modeling heuristic primary location model home and work locations human mobility pattern urban activity pattern

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

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Geo-Spatial Information Science 2021年第4期24卷 711-721页

作者： nilufer sari aslam Mohamed R.Ibrahim Tao Cheng Huanfa Chen Yang Zhang SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)LondonUK Centre for Advanced Spatial Analysis(CASA) University College LondonLondonUK

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban ***,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)*** feasibility of the framework is demonstrated in two *** 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 *** a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip *** 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

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