Passenger Flow Direction Analysis of Urban City Using Bus Card Data
Abstract: While Automated Fare Collection System is widely used in many cities all over the world, large amount of swiping card data has been recorded. By using the data, this paper proposed a novel method to recognize passenger flow direction in urban cities. At the beginning of this paper, subway stations are divided into three kinds, including stations in residences, stations in work area and stations in mixed area. Then a model is built to recognize the passenger flow directions in peak hour. Finally, passenger flow patterns in Beijing are analyzed.
文章引用: 李 曼 , 陈志宏 , 隋莉颖 (2012) 基于一卡通数据的城市客流流向分析。 无线通信， 2， 57-64. doi: 10.12677/hjwc.2012.24011
 N. Lathia, L. Capra. How smart is your smartcard? Measuring travel behaviours, perceptions, and incentives. 13th ACM Inter- national Conference on Ubiquitous Computing (Ubicomp), Bei- jing, September 2011.
 M. Bagchi. Use of smartcard data from bus systems for travel behavior analysis, and implications for marketing. Ph.D. Thesis, University of Westminster, 2003, unpublished.
 M. Bagchi, P. R. White. Use of public transports smart card data for understanding travel behavior. Proceedings of the European Transport Conference, Strasbourg, 8-10 October 2003.
 J. Chan. Rail transit OD matrix estimation and journey time reliability metrics using automated fare data. Master’s Thesis, Massachusetts Institute of Technology (MIT), 2007.
 C. Ratti, L. Liu, A. Hou, A. Biderman, J. Chen, et al. Under- standing individual and collective mobility patterns from smart card records: A case study in Shenzhen. 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, 3-7 October 2009: 1-6.
 T. Garling, K. W. Axhausen. Introduction: Habitual travel choice. Transportation, 2003, 30(1): 1-11.
 R. Kitamura, T. Yamamoto, Y. O. Susilo and K. W. Axhausen. Howroutine is a routine? An analysis of the day-to-day variability in prismvertex location. Transportation Research Part A, 2006, 40: 259-279.