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Detecting public transport passenger movement patterns. / Grafeeva, Natalia; Mikhailova, Elena.

Trends and Innovations in Information Systems and Technologies . Vol. 2 Springer Nature, 2020. p. 555-563 (Advances in Intelligent Systems and Computing; Vol. 1160).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Grafeeva, N & Mikhailova, E 2020, Detecting public transport passenger movement patterns. in Trends and Innovations in Information Systems and Technologies . vol. 2, Advances in Intelligent Systems and Computing, vol. 1160, Springer Nature, pp. 555-563, Trends and Innovations in Information Systems and Technologies, Budva, Montenegro, 7/04/20. https://doi.org/10.1007/978-3-030-45691-7_52

APA

Grafeeva, N., & Mikhailova, E. (2020). Detecting public transport passenger movement patterns. In Trends and Innovations in Information Systems and Technologies (Vol. 2, pp. 555-563). (Advances in Intelligent Systems and Computing; Vol. 1160). Springer Nature. https://doi.org/10.1007/978-3-030-45691-7_52

Vancouver

Grafeeva N, Mikhailova E. Detecting public transport passenger movement patterns. In Trends and Innovations in Information Systems and Technologies . Vol. 2. Springer Nature. 2020. p. 555-563. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-45691-7_52

Author

Grafeeva, Natalia ; Mikhailova, Elena. / Detecting public transport passenger movement patterns. Trends and Innovations in Information Systems and Technologies . Vol. 2 Springer Nature, 2020. pp. 555-563 (Advances in Intelligent Systems and Computing).

BibTeX

@inproceedings{b628154231fb49e4be6fe3274bee049b,
title = "Detecting public transport passenger movement patterns",
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.",
keywords = "Multimodal trips, Pattern mining, Public transport, Urban transit system",
author = "Natalia Grafeeva and Elena Mikhailova",
note = "Grafeeva, N., Mikhailova, E. (2020). Detecting Public Transport Passenger Movement Patterns. In: Rocha, {\'A}., 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; Trends and Innovations in Information Systems and Technologies ; Conference date: 07-04-2020 Through 10-04-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-45691-7_52",
language = "English",
isbn = "978-3-030-45690-0",
volume = "2",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "555--563",
booktitle = "Trends and Innovations in Information Systems and Technologies",
address = "Germany",

}

RIS

TY - GEN

T1 - Detecting public transport passenger movement patterns

AU - Grafeeva, Natalia

AU - Mikhailova, Elena

N1 - 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

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Multimodal trips

KW - Pattern mining

KW - Public transport

KW - Urban transit system

UR - http://www.scopus.com/inward/record.url?scp=85086222482&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-45691-7_52

DO - 10.1007/978-3-030-45691-7_52

M3 - Conference contribution

AN - SCOPUS:85086222482

SN - 978-3-030-45690-0

VL - 2

T3 - Advances in Intelligent Systems and Computing

SP - 555

EP - 563

BT - Trends and Innovations in Information Systems and Technologies

PB - Springer Nature

T2 - Trends and Innovations in Information Systems and Technologies

Y2 - 7 April 2020 through 10 April 2020

ER -

ID: 103098236