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
• 来源期刊: 地球空间信息科学学报（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: Articles
• 页码范围: 711-721
• 页数: 11页
• 语种: 英文

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 passen-gers' 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.