Abstract
Most fare collection systems are initially installed as single-purpose devices which are only used for collecting fare; however, many transit planners consider them as a rich source of data required for studying the passengers' trip trends. Although, usually, there is no transaction made at the destination stop, making some assumptions can help us infer the destination. In this study, we present an integrated procedure that can generate origin–destination matrices and passenger load profiles as essential tools for public transport planning processes. Moreover, this procedure can be used to detect and analyze trips that include transfers. In an attempt to employ the proposed algorithm in the Tehran bus rapid transit network, 52% of the transactions could be used to trace the trips in an origin–destination format. The trips that include transfers are recognized and analyzed further. Our detailed results of the method application indicate that the proposed algorithm is a productive and economical public transport planning method.
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References
Alsger AA, Mesbah M, Ferreira L, Safi H (2015) Use of smart card fare data to estimate public transport origin–destination matrix. Transport Res Record J Transport Res Board 2535:88–96. https://doi.org/10.3141/2535-10
Alsger A, Assemi B, Mesbah M, Ferreira L (2016) Validating and improving public transport origin–destination estimation algorithm using smart card fare data. Transport Res Part C Emerg Technol. https://doi.org/10.1016/j.trc.2016.05.004
Alsger A, Tavassoli A, Mesbah M, Ferreira L (2017) Evaluation of effects from sample-size origin–destination estimation using smart card fare data. J Transport Eng Part A Syst 143(4):04017003
Bagchi M, White PR (2005) The potential of public transport smart card data. Transp Policy 12:464–474. https://doi.org/10.1016/j.tranpol.2005.06.008
Barry JJ, Freimer R, Slavin H (2009) Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. Transport Res Record J Transport Res Board 2112:53–61. https://doi.org/10.3141/2112-07
Covic F, Voß S (2019) Interoperable smart card data management in public mass transit. Public Transport 11:523–548. https://doi.org/10.1007/s12469-019-00216-x
Cui A (2006) Bus passenger origin-destination matrix estimation using automatic data collection systems (Doctoral dissertation). Massachusetts Institute of Technology
Ding S, Huang H, Zhao T, Fu X (2019) Estimating socioeconomic status via temporal-spatial mobility analysis—a case study of smart card data. In: 2019 28th international conference on computer communication and networks (ICCCN). IEEE, pp 1–9. https://doi.org/10.1109/ICCCN.2019.8847051
Fabbiani E, Nesmachnow S, Toutouh J, Tchernykh A, Avetisyan A, Radchenko G (2018) Analysis of mobility patterns for public transportation and bus stops relocation. Program Comput Softw 44(6):508–525. https://doi.org/10.1134/S0361768819010031
Gordon JJ, Koutsopoulos H, Wilson N, Attanucci J (2013) Automated inference of linked transit journeys in London using fare-transaction and vehicle location data. Transport Res Record J Transport Res Board 2343(1):17–24. https://doi.org/10.3141/2343-03
Goulet Langlois G, Koutsopoulos HN, Zhao J (2016) Inferring patterns in the multi-week activity sequences of public transport users. Transport Res Part C Emerg Technol 64:1–16. https://doi.org/10.1016/j.trc.2015.12.012
Habibian M, Hosseinzadeh A (2018) Walkability index across trip purposes. Sustain Cities Soc 42(July):216–225. https://doi.org/10.1016/j.scs.2018.07.005
Halvorsen A, Koutsopoulos HN, Ma Z, Zhao J (2019) Demand management of congested public transport systems: a conceptual framework and application using smart card data. Transportation. https://doi.org/10.1007/s11116-019-10017-7
Ilbeigi M, Ebrahimi Meimand M (2020) Statistical forecasting of bridge deterioration conditions. J Perform Construct Facil 34(1):04019104
Kieu LM, Bhaskar A, Chung E (2015) A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data. Transport Res Part C Emerg Technol 58:193–207. https://doi.org/10.1016/j.trc.2015.03.033
Kusakabe T, Asakura Y (2014) Behavioural data mining of transit smart card data: a data fusion approach. Transport Res Part C Emerg Technol 46:179–191
Morency C, Trépanier M, Agard B (2006) Analysing the variability of transit users behaviour with smart card data. In: 2006 IEEE intelligent transportation systems conference. IEEE, pp 44–49
Munizaga MA, Palma C (2012) Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile. Transport Res Part C Emerg Technol 24:9–18. https://doi.org/10.1016/j.trc.2012.01.007
Munizaga M, Devillaine F, Navarrete C, Silva D (2014) Validating travel behavior estimated from smartcard data. Transport Res Part C Emerg Technol 44:70–79. https://doi.org/10.1016/j.trc.2014.03.008
Nassir N, Khani A, Lee SG, Noh H, Hickman M (2012) Transit stop-level origin–destination estimation through use of transit schedule and automated data collection system. Transport Res Record J Transport Res Board 2263(-1):140–150. https://doi.org/10.3141/2263-16
Nassir N, Hickman M, Ma ZL (2015) Activity detection and transfer identification for public transit fare card data. Transportation 42(4):683–705. https://doi.org/10.1007/s11116-015-9601-6
Parsa AB, Taghipour H, Derrible S, Mohammadian A (2019) Real-time accident detection: coping with imbalanced data. Accid Anal Prev 129(January):202–210. https://doi.org/10.1016/j.aap.2019.05.014
Rahimi E, Shamshiripour A, Shabanpour R, Mohammadian A, Auld J (2019) Analysis of transit users’ waiting tolerance in response to unplanned service disruptions. Transport Res Part D. https://doi.org/10.1016/j.trd.2019.10.011
Syarif M, Widyawan, Adji TB (2019) Big data analytics: estimation of destination for users of bus rapid transit (BRT) public transportation in Jakarta. In: Proceeding—2019 international conference of artificial intelligence and information technology, ICAIIT 2019, 209–214. https://doi.org/10.1109/ICAIIT.2019.8834591
Trépanier M, Tranchant N, Chapleau R (2007) Individual trip destination estimation in a transit smart card automated fare collection system. J Intell Transport Syst Technol Plan Oper 11(1):1–14. https://doi.org/10.1080/15472450601122256
Wang W, Attanucci JP, Wilson NHM (2011) Bus passenger origin–destination estimation and related analyses using automated data collection systems. J Public Transport 14(4):131–150. https://doi.org/10.5038/2375-0901.14.4.7
Zhang Y, Martens K, Long Y (2018) Revealing group travel behavior patterns with public transit smart card data. Travel Behav Soc 10(2018):42–52. https://doi.org/10.1016/j.tbs.2017.10.001
Zhao J, Rahbee A, Wilson NH (2007) Estimating a rail passenger trip origin–destination matrix using automatic data collection systems. Comput Aided Civ Infrastruct Eng 22(5):376–387. https://doi.org/10.1111/j.1467-8667.2007.00494.x
Zhao X, Zhang Y, Liu H, Wang S, Qian Z, Hu Y, Yin B (2019) Detecting pickpocketing gangs on buses with smart card data. IEEE Intell Transp Syst Mag 11(June):181–199. https://doi.org/10.1109/MITS.2019.2919525
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MJK: data collection and analysis, research for developing the methodology, manuscript writing and editing, pseudo-code generating. SA: developing the methodology and analyzing results, literature search and review, manuscript writing and editing. SMMA: supervisor of method development and result analysis, manuscript editing.
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Jafari Kang, M., Ataeian, S. & Amiripour, S.M.M. A procedure for public transit OD matrix generation using smart card transaction data. Public Transp 13, 81–100 (2021). https://doi.org/10.1007/s12469-020-00257-7
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DOI: https://doi.org/10.1007/s12469-020-00257-7