首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Unified estimator for excess journey time under heterogeneous passenger incidence behavior using smartcard data
Institution:1. Department of Civil Engineering, University of British Columbia, 2007-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada;2. Metropolitan Transportation Authority Bus Customer Information Systems, 2 Broadway, 27th Floor, New York, NY 10004, USA;3. Department of Civil and Environmental Engineering, MIT, 1-238, 77 Massachusetts Avenue, Cambridge, MA 02139, USA;4. Department of Civil Engineering, University of British Columbia, 2002-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada;1. RIKEN, Advanced Institute for Computational Science, Kobe, Japan;2. Kobe University, Kobe, Japan;3. Fujitsu Systems East Co.Ltd., Nagano, Japan;4. University of Tokyo, Institute of Industrial Science, Tokyo, Japan;1. Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Northeastern University, Boston, MA 02115, USA;9. Divisao de Molestias Infecciosas, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;99. Subcomite de Infeccao em Imunodeprimidos, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;999. Instituto da Crianca (ICr), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;9V. Departamento de Molestias Infecciosas, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BR;V. Servico de Transplante Renal, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;V9. Nucleo de Transplante Cardiaco, Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;V99. Servico de Pneumologia, Grupo de Transplante Pulmonar, Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;V999. Divisao de Transplante de Figado e Orgaos do Aparelho Digestivo, Departamento de Gastroenterologia, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;9X. Servico de Imunologia, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;X. Servico de Biologia Molecular, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;X9. Grupo Controle de Infeccao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR;1. Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, Jiangsu 210096, PR China;2. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA;1. Department of Mathematics, Kwame Nkrumah University of Science and Technology, Ghana;2. CSIR-Building and Road Research Institute, Ghana;3. Department of Economics and Statistics, Garden City University College, Ghana
Abstract:Excess journey time (EJT), the difference between actual passenger journey times and journey times implied by the published timetable, strikes a useful balance between the passenger’s and operator’s perspectives of public transport service quality. Using smartcard data, this paper tried to characterize transit service quality with EJT under heterogeneous incidence behavior (arrival at boarding stations). A rigorous framework was established for analyzing EJT, in particular for reasoning about passenger’ journey time standards as implied by varying incidence behavior. It was found that although the wrong assumption about passenger incidence behavior and journey time standards could result in a biased estimate of EJT for individual passenger journeys, the unified estimator of EJT proposed in this paper is unbiased at the aggregate level regardless of the passenger incidence behavior (random incidence, scheduled incidence, or a mixture of both). A case study based on the London Overground network (with a tap-in-and-tap-out smartcard system) was conducted to demonstrate the applicability of the proposed method. EJT was estimated using the smartcard (Oyster) data at various levels of spatial and temporal aggregation in order to measure and evaluate the service quality. Aggregate EJT was found to vary substantially across the different London Overground lines and across time periods of weekday service.
Keywords:Excess journey time  Service quality  Passenger incidence behavior  Smartcard data  London Overground
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号