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Detecting Public Transport Passenger Movement Patterns

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Book cover Trends and Innovations in Information Systems and Technologies (WorldCIST 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1160))

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Abstract

In this paper, we analyze public transport passenger movement data to detect typical patterns. The initial data consists of smart card transactions made upon entering public transport, collected over the course of two weeks in Saint Petersburg, a city with a population of 5 million. As a result of the study, we detected 5 classes of typical passenger movement between home and work, with the scale of one day. Each class, in turn, was clusterized in accordance with the temporal habits of passengers. Heat maps were used to demonstrate clusterization results. The results obtained in the paper can be used to optimize the transport network of the city being studied, and the approach itself, based on clusterization algorithms and using heat maps to visualize the results, can be applied to analyze public transport passenger movement in other cities.

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Author information

Authors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Natalia Grafeeva & Elena Mikhailova

Authors
  1. Natalia Grafeeva
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  2. Elena Mikhailova
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Corresponding author

Correspondence to Natalia Grafeeva .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. FEUP, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

  5. Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro

    Irena Orovic

  6. Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Grafeeva, N., Mikhailova, E. (2020). Detecting Public Transport Passenger Movement Patterns. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_52

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