Understanding Daily Travel Patterns of Subway Users – An Example from the Beijing Subway
The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.
Long Y, Thill J. Combining smart card data and household travel survey to analyse jobs–housing relationships in Beijing. Computers, Environment and Urban Systems. 2015;53: 19-35.
Sun Y, Shi J, Schonfeld PM. Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro. Public Transportation. 2016;8: 341-363.
Beijing Transportation Development Annual Report. Beijing Transportation Research Center; 2016. Chinese.
Shanghai Comprehensive Traffic Operation Annual Report. Shanghai Urban and Rural Construction and Transportation Development Research Institute; 2017. Chinese.
Jiang ZB, Liu W, Zhu BQ. Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours. Transportation Research Part C. 2018;88: 1-16.
Xu XY, Liu J, Li HY, Jiang M. Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study. Transportation Research Part E. 2016;87: 130-148.
Boyle DK, Foote PJ, Karash KH. Public transportation marketing and fare policy. Transport in the New Millennium. 2000;A1E06.
Ma XL, Wu YJ, Wang YH, Chen F, Liu JF. Mining smart card data for transit riders’ travel patterns. Transportation Research Part C. 2013;36: 1-12.
Yang Y, Herrera C, Eagle N, González MC. Limits of predictability in commuting flows in the absence of data for calibration. Scientific Reports. 2014;4: 5662.
Ren Y, Ercsey-Ravasz M, Wang P, González MC, Toroczkai Z. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges. Nature Communications. 2014;5: 5347.
Ortega-Tong MA. Classification of London’s public transport users using smart card data. S.M. Thesis. Cambridge, MA: Massachusetts Institute of Technology; 2013.
Ma XL, Liu CC, Wen HM, Wang YP, Wu YJ. Understanding commuting patterns using transit smart card data. Journal of Transport Geography. 2017;58: 135-145.
Goulet-Langlois G, Koutsopoulos HN, Zhao J. Inferring patterns in the multi-week activity sequences of public transport users. Transportation Research Part C. 2016;64: 1-16.
Liang Q, Weng JC, Lin PF, Zhou W, Rong J. Public transport commuter identification based on individual travel graph. Journal of Transportation Systems Engineering and Information Technology. 2018;18(2): 100-107. Chinese.
Zhou QR, Zhao P, Yao XM. Passenger classification for urban rail transit by mining smart card data. Journal of Transportation Systems Engineering and Information Technology. 2018;18(1): 223-230. Chinese.
Briand A, Côme E, Trépanier M, Oukhellou L. Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transportation Research Part C. 2017;79: 274-289.
Morency C, Trépanier M, Agard B. Measuring transit use variability with smart-card data. Transport Policy. 2007;14: 193-203.
Kusakabe T, Asakura Y. Behavioural data mining of transit smart card data: A data fusion approach. Transportation Research Part C. 2014;46: 179-191.
Zhao JJ, Qu Q, Zhang F, Xu CZ, Liu SY. Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Transactions on Intelligent Transportation Systems. 2017;18(11): 3135-3146.
Li M, Wang YH, Jia LM. The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory. PLOS ONE. 2017;12(9): e0184131.
McGuckin N, Nakamoto Y. Trips, chains, and tours: using an operational definition. National Household Travel Survey: Understanding Our Nation's Travel (NHTS), 1-2 November 2004, Washington DC, USA; 2004.
Weng JC, Wang C, Wang YY, Chen ZH, Peng S. Extraction method of public transit trip chains based on the individual riders’ data. Journal of Transportation Systems Engineering and Information Technology. 2017;17(3): 67-73. Chinese.
Barry J, Newhouser R, Rahbee A, Sayeda S. Origin and destination estimation in New York City with automated fare system data. Transportation Research Record. 2002;1817: 183-187.
Liu MJ, Mao BH, Gao F, Guo JY, Gao LP. Analysis on commuter’s activity chain choice behaviour. Sixth International Conference of Traffic and Transportation Studies Congress (ICTTS), 5-7 August 2008, Nanning, China. American Society of Civil Engineers; 2008. p. 222-230.
Yao XP, Zhao P, Han BM, Zhou QR. Home district identification for urban rail transit travellers by mining automatic fare collection data. Journal of Transportation Systems Engineering and Information Technology. 2016;16(5): 233-240. Chinese.
Kitamura R. Sequential, history dependent approach to trip chaining behaviour. Transportation Research Record. 1983;944: 13-22.
Stead D, Marshall S. The Relationships between urban form and travel patterns. An international review and evaluation. European Journal of Transport and Infrastructure Research. 2001;1(2): 113-141.
Mao ZD, Ettema D, Dijst M. Analysis of travel time and mode choice shift for non-work stops in commuting: Case study of Beijing, China. Transportation. 2018;45: 751-766.
Hsu CI, Guo SP. CBD oriented commuters’ mode and residential location choices in an urban area with surface streets and rail transit lines. Journal of Urban Planning and Development. 2006;132(4): 235-246.
Sun YS, Schonfeld PM. Schedule-based rail transit path-choice estimation using automatic fare collection data. Journal of Transportation Engineering. 2016;142(1): 04015037-1-8.
Thorhauge M, Cherch E, Rich J. How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips. Transportation Research Part A: Policy and Practice. 2016;86: 177-193.
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