Activity Modelling Using Journey Pairing of Taxi Trajectory Data

Shuhui Gong; John Cartlidge; Ruibin Bai; Yang Yue; Qingquan Li; Guoping Qiu

doi:10.1109/ICBDA.2019.8712832

Activity Modelling Using Journey Pairing of Taxi Trajectory Data

Shuhui Gong, John Cartlidge, Ruibin Bai, Yang Yue, Qingquan Li, Guoping Qiu

School of Computer Science

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

5 Citations (Scopus)

Abstract

Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a novel method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.

Original language	English
Title of host publication	2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	236-240
Number of pages	5
ISBN (Electronic)	9781728112824
DOIs	https://doi.org/10.1109/ICBDA.2019.8712832
Publication status	Published - 10 May 2019
Event	4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China Duration: 15 Mar 2019 → 18 Mar 2019

Publication series

Name	2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

Conference	4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Country/Territory	China
City	Suzhou
Period	15/03/19 → 18/03/19

Keywords

Monte Carlo simulation
Power law distance decay function
travel behaviour analysis

ASJC Scopus subject areas

Artificial Intelligence
Information Systems
Information Systems and Management
Statistics, Probability and Uncertainty

Access to Document

10.1109/ICBDA.2019.8712832

Cite this

Gong, S., Cartlidge, J., Bai, R., Yue, Y., Li, Q., & Qiu, G. (2019). Activity Modelling Using Journey Pairing of Taxi Trajectory Data. In 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 (pp. 236-240). Article 8712832 (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2019.8712832

@inproceedings{b84b877770bf48c6971910bc14d77faa,

title = "Activity Modelling Using Journey Pairing of Taxi Trajectory Data",

abstract = "Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a novel method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.",

keywords = "Monte Carlo simulation, Power law distance decay function, travel behaviour analysis",

author = "Shuhui Gong and John Cartlidge and Ruibin Bai and Yang Yue and Qingquan Li and Guoping Qiu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",

year = "2019",

month = may,

day = "10",

doi = "10.1109/ICBDA.2019.8712832",

language = "English",

series = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",

address = "United States",

}

Gong, S, Cartlidge, J, Bai, R, Yue, Y, Li, Q & Qiu, G 2019, Activity Modelling Using Journey Pairing of Taxi Trajectory Data. in 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019., 8712832, 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019, Institute of Electrical and Electronics Engineers Inc., pp. 236-240, 4th IEEE International Conference on Big Data Analytics, ICBDA 2019, Suzhou, China, 15/03/19. https://doi.org/10.1109/ICBDA.2019.8712832

Activity Modelling Using Journey Pairing of Taxi Trajectory Data. / Gong, Shuhui; Cartlidge, John; Bai, Ruibin et al.
2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 236-240 8712832 (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019).

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

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T1 - Activity Modelling Using Journey Pairing of Taxi Trajectory Data

AU - Gong, Shuhui

AU - Cartlidge, John

AU - Bai, Ruibin

AU - Yue, Yang

AU - Li, Qingquan

AU - Qiu, Guoping

PY - 2019/5/10

Y1 - 2019/5/10

N2 - Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a novel method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.

AB - Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a novel method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.

KW - Monte Carlo simulation

KW - Power law distance decay function

KW - travel behaviour analysis

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M3 - Conference contribution

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T3 - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

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EP - 240

BT - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Gong S, Cartlidge J, Bai R, Yue Y, Li Q, Qiu G. Activity Modelling Using Journey Pairing of Taxi Trajectory Data. In 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 236-240. 8712832. (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019). doi: 10.1109/ICBDA.2019.8712832

Activity Modelling Using Journey Pairing of Taxi Trajectory Data

Abstract

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Keywords

ASJC Scopus subject areas

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