2026-09-19T11:35:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/828442026-06-06T01:06:35Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nigel H. M. Wilson and Harilaos N. Koutsopoulos.
Ortega-Tong, Meisy A. (Meisy Andrea)
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
e-uk-en
2013-12-06T20:48:31Z
2013-12-06T20:48:31Z
2013
http://hdl.handle.net/1721.1/82844
863226764
Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 157-163).
Understanding transit users in terms of their travel patterns can support the planning and design of better services. User classification can improve market research through more targeted access to groups of interest. It facilitates planning through better survey design, as well as more detailed evaluation, through analysis of impacts based on the characterization of the affected users. Classification of public transport users can be enhanced through the use of data from smart cards. The objective of the thesis is to categorize and better understand travel patterns of London's public transport users, using an extensive database of Oyster Card transactions. Several travel characteristics related to temporal and spatial variability, activity patterns, sociodemographic characteristics, and mode choices are used to identify homogeneous clusters. Four of the groups identified represent regular users composed of workers and students who make commuting journeys during the week, and some of them make leisure journeys during weekends. The four remaining clusters are occasional users, composed of leisure travelers, and visitors traveling for tourism and business purposes. A detailed analysis of the characteristics of each group in terms of spatial travel patterns, temporal changes in cluster characteristics, and membership is presented. Lack of temporal stability at the cluster level indicated that four clusters are more appropriate to analyze passenger behavior. The clusters were used to examine in detail characteristics of some special groups, such as visitors and registered users. Visitors belong mainly to two clusters, making it possible to identify business and leisure visitors. Registered users showed larger proportions in regular user clusters and their travel patterns were more similar to regular user behavior. The analysis of Oyster Card attrition rates showed that occasional user cards exit the system at a faster rate than cards of regular users who retain their cards for longer periods of time, explaining the high drop in the number of active Oyster Cards observed between consecutive months.
by Meisy A. Ortega-Tong.
S.M. in Transportation
163 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Classification of London's public transport users using smart card data
Thesis
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<Abstract>Understanding transit users in terms of their travel patterns can support the planning and design of better services. User classification can improve market research through more targeted access to groups of interest. It facilitates planning through better survey design, as well as more detailed evaluation, through analysis of impacts based on the characterization of the affected users. Classification of public transport users can be enhanced through the use of data from smart cards. The objective of the thesis is to categorize and better understand travel patterns of London's public transport users, using an extensive database of Oyster Card transactions. Several travel characteristics related to temporal and spatial variability, activity patterns, sociodemographic characteristics, and mode choices are used to identify homogeneous clusters. Four of the groups identified represent regular users composed of workers and students who make commuting journeys during the week, and some of them make leisure journeys during weekends. The four remaining clusters are occasional users, composed of leisure travelers, and visitors traveling for tourism and business purposes. A detailed analysis of the characteristics of each group in terms of spatial travel patterns, temporal changes in cluster characteristics, and membership is presented. Lack of temporal stability at the cluster level indicated that four clusters are more appropriate to analyze passenger behavior. The clusters were used to examine in detail characteristics of some special groups, such as visitors and registered users. Visitors belong mainly to two clusters, making it possible to identify business and leisure visitors. Registered users showed larger proportions in regular user clusters and their travel patterns were more similar to regular user behavior. The analysis of Oyster Card attrition rates showed that occasional user cards exit the system at a faster rate than cards of regular users who retain their cards for longer periods of time, explaining the high drop in the number of active Oyster Cards observed between consecutive months.</Abstract>
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