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Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference

  • Published:2021-03 Issue: Volume:124 Page:102921
  • ISSN:0968-090X
  • Container-title:Transportation Research Part C: Emerging Technologies
  • language:en
  • Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Li Can,Bai Lei,Liu Wei,Yao Lina,Travis Waller S.

Funder

Australian Research Council

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference59 articles.

1. Observing the rhythms of daily life: a six-week travel diary;Axhausen;Transportation,2002

2. Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction;Bai,2019

3. Stg2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting;Bai,2019

4. Passenger demand forecasting with multi-task convolutional recurrent neural networks;Bai,2019

5. City attachment and use of urban services: benefits for smart cities;Belanche;Cities,2016

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1. Understanding the timing of urban morning commuting trips on mass transit railway systems;Transportation Research Part C: Emerging Technologies;2024-02

2. Income estimation based on human mobility patterns and machine learning models;Applied Geography;2024-02

3. A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery;Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data;2023-11-13

4. Urban Knowledge Graph Aided Mobile User Profiling;ACM Transactions on Knowledge Discovery from Data;2023-10-16

5. Identifying human mobility patterns using smart card data;Transport Reviews;2023-08-28
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