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

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

5 Citations (Scopus)

403 Downloads (Pure)

Abstract

Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, the raw 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 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, reveal behavioural patterns and suggest possibilities for applications in urban design.

Original language	English
Title of host publication	2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019)
Subtitle of host publication	Proceedings of a meeting held 9-12 March 2018, Shanghai, China
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	236-240
Number of pages	5
ISBN (Electronic)	9781728112824
ISBN (Print)	9781728112831
DOIs	https://doi.org/10.1109/ICBDA.2019.8712832
Publication status	Published - Jun 2019
Event	4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China Duration: 15 Mar 2019 → 18 Mar 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

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10.1109/ICBDA.2019.8712832

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Gong, S., Cartlidge, J., Bai, R., Yue, Y., Li, Q., & Qiu, G. (2019). Activity Modelling Using Journey Pairing of Taxi Trajectory Data. In 2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019): Proceedings of a meeting held 9-12 March 2018, Shanghai, China (pp. 236-240). Article 8712832 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICBDA.2019.8712832

@inproceedings{62fd981d448e4cd19a8dd3da6e03c996,

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, the raw 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 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, 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",

year = "2019",

month = jun,

doi = "10.1109/ICBDA.2019.8712832",

language = "English",

isbn = "9781728112831",

pages = "236--240",

booktitle = "2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",

}

Gong, S, Cartlidge, J, Bai, R, Yue, Y, Li, Q & Qiu, G 2019, Activity Modelling Using Journey Pairing of Taxi Trajectory Data. in 2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019): Proceedings of a meeting held 9-12 March 2018, Shanghai, China., 8712832, Institute of Electrical and Electronics Engineers (IEEE), 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 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019): Proceedings of a meeting held 9-12 March 2018, Shanghai, China. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 236-240 8712832.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

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 - 2019/6

Y1 - 2019/6

N2 - Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, the raw 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 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, 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, the raw 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 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, 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

UR - https://www.scopus.com/pages/publications/85066636780

U2 - 10.1109/ICBDA.2019.8712832

DO - 10.1109/ICBDA.2019.8712832

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85066636780

SN - 9781728112831

SP - 236

EP - 240

BT - 2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Y2 - 15 March 2019 through 18 March 2019

ER -

Gong S, Cartlidge J, Bai R, Yue Y, Li Q, Qiu G. Activity Modelling Using Journey Pairing of Taxi Trajectory Data. In 2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019): Proceedings of a meeting held 9-12 March 2018, Shanghai, China. Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 236-240. 8712832 Epub 2019 May 13. doi: 10.1109/ICBDA.2019.8712832

Activity Modelling Using Journey Pairing of Taxi Trajectory Data

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