Characterising and predicting urban mobility dynamics by mining bike sharing system data

Ida Bagus Irawan Purnama, Neil Bergmann, Raja Jurdak, Kun Zhao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

Understanding the spatio-temporal characteristics of human mobility in urban areas is invaluable especially for traffic management and urban planning. An opportunity to characterize and predict urban mobility is provided by mining Bike Sharing System (BSS) trip data spatially and temporally. This study focuses on identifying highly predictable BSS users, revealing their mobility characteristics and predicting their next-place movements. In undertaking user classification, we compare between naïve subscription based classification, namely registered and unregistered users, and trip number based classification, namely commuter, regular and casual users. Our analysis of 13 weeks of London BSS data shows that users with high predictability are those with 50 or more historical trips and with commuting patterns that can be fairly represented by registered users. However, because of the diversity of usage patterns, the spatio-temporal mobility characteristics of registered users are not as uniform as commuters who are the fastest, farthest travelled, most consistent, and most predictable riders with Markovian qualities. Using only a first order Markov predictor, the prediction of trip destination based on trip origin in weekday morning is up to 78% predictable for commuters.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops
Subtitle of host publicationUIC-ATC-ScalCom-CBDCom-IoP 2015
EditorsJianhua Ma, Laurence T. Yang, Huansheng Ning, Ali Li
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages159-167
Number of pages9
ISBN (Electronic)9781467372114
ISBN (Print)9781467372121
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015 - Beijing, China
Duration: 10 Aug 201514 Aug 2015

Other

OtherProceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
CountryChina
CityBeijing
Period10/08/1514/08/15

Keywords

  • Bike sharing system
  • Characterization
  • Commuter
  • Data mining
  • Entropy
  • Markov predictor
  • Next-place prediction
  • Predictability
  • Urban mobility dynamics

Fingerprint Dive into the research topics of 'Characterising and predicting urban mobility dynamics by mining bike sharing system data'. Together they form a unique fingerprint.

Cite this