@inproceedings{
   author = {Agard, Bruno and Morency, Catherine and Trépanier, Martin},
   title = {Mining public transport user behaviour from smart card data},
   booktitle = {12th IFAC symposium on information control problems in manufacturing-INCOM},
   pages = {17-19},
   type = {Conference Proceedings}
}

@article{
   author = {Arana, P and Cabezudo, S and Peñalba, M},
   title = {Influence of weather conditions on transit ridership: A statistical study using data from Smartcards},
   journal = {Transportation Research Part A: Policy and Practice},
   volume = {59},
   pages = {1-12},
   ISSN = {09658564},
   DOI = {10.1016/j.tra.2013.10.019},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Bagchi, M and White, P R},
   title = {The potential of public transport smart card data},
   journal = {Transport Policy},
   volume = {12},
   number = {5},
   pages = {464-474},
   ISSN = {0967070X},
   DOI = {10.1016/j.tranpol.2005.06.008},
   year = {2005},
   type = {Journal Article}
}

@article{
   author = {Barry, James J and Newhouser, Robert and Rahbee, Adam and Sayeda, Shermeen},
   title = {Origin and destination estimation in New York City with automated fare system data},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {1817},
   number = {1},
   pages = {183-187},
   ISSN = {0361-1981},
   year = {2002},
   type = {Journal Article}
}

@article{
   author = {Bin Othman, Nasri and Legara, Erika Fille and Selvam, Vicknesh and Monterola, Christopher},
   title = {Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data},
   journal = {2014 International Conference on Computational Science},
   volume = {29},
   pages = {1610-1620},
   note = {Times Cited: 0
Abramson, D Lees, M Krzhizhanovskaya, VV Dongarra, J Sloot, PMA
14th Annual International Conference on Computational Science
JUN 10-12, 2014
Cairns, AUSTRALIA
Univ Queensland; Univ Amsterdam; NTU Singapore; Univ Tennessee
0
*****************},
   abstract = {Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains-all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case.},
   ISSN = {1877-0509},
   DOI = {10.1016/j.procs.2014.05.146},
   url = {<Go to ISI>://WOS:000341492700146},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Blythe, Philip T},
   title = {Improving public transport ticketing through smart cards},
   journal = {Proceedings of the ICE-Municipal Engineer},
   volume = {157},
   number = {1},
   pages = {47-54},
   ISSN = {1751-7699},
   year = {2004},
   type = {Journal Article}
}

@article{
   author = {Bryan, H. and Blythe, P.},
   title = {Understanding behaviour through smartcard data analysis},
   journal = {Proceedings of the ICE - Transport},
   volume = {160},
   number = {4},
   pages = {173-177},
   ISSN = {0965-092X
1751-7710},
   DOI = {10.1680/tran.2007.160.4.173},
   year = {2007},
   type = {Journal Article}
}

@article{
   author = {Chapleau, Robert and Alfred Chu, Ka Kee},
   title = {Enriching Archived Smart Card Transaction Data for Transit Demand Modeling},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2063},
   number = {-1},
   pages = {63-72},
   ISSN = {0361-1981},
   DOI = {10.3141/2063-08},
   year = {2008},
   type = {Journal Article}
}

@article{
   author = {Chapleau, Robert and Chu, Ka Kee Alfred},
   title = {Augmenting Transit Trip Characterization and Travel Behavior Comprehension},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2183},
   number = {-1},
   pages = {29-40},
   ISSN = {0361-1981},
   DOI = {10.3141/2183-04},
   year = {2010},
   type = {Journal Article}
}

@article{
   author = {Chen, Jun and Yang, Dongyuan},
   title = {Method for identifying transfer stops of passengers with smart cards integrating intelligent bus dispatching data},
   journal = {Journal of Chang'An University. Natural Science Edition},
   volume = {33},
   number = {4},
   pages = {92-98},
   note = {Times Cited: 0
长安大学学报. 自然科学版
0},
   abstract = {In order to get transfer information by APTS (advanced public transportation systems) data, the data of intelligent dispatching system and automatic collection system were collected,the data structure and quality were analyzed, and the data were organized by data warehouse technology. Based on the analysis of fundamental idea and parameters calculation of transfer judgment, the paper put forward the method of identifying transfer stops by accurately calculating reasonable transfer time between two continuous boarding. Finally, the algorithm of identifying transfer stops was presented, programmed and tested on large scale actual APTS data of Nanning City of China. The results indicate that this method can reliably calculate the transfer threshold value by matching bus GPS data and bus stop coordinates; the judgment errors of bus travel time and waiting bus time are less than 60 s; this method can be used to identify whether the two boarding will have a transfer behavior and also to determine the transfer stops and the transfer time, and the accuracy is greatly improved compared with traditional uniform threshold method. 1 tab, 8 figs, 12 refs.
为应用智能公交系统数据获得公交乘客的换乘信息,采集公交智能调度系统和公交自动收费系统数据,对原始数据结构和数据质量进行分析,应用数据仓库技术完成 原始数据组织;在对公交乘客换乘站点判断思路和参数计算方法研究的基础上,提出了通过计算逐个乘客连续2次乘车的合理换乘时间来判断换乘站点的方法,构建 了实现算法并进行编程,最后以南宁市智能公交系统的海量实际数据对算法进行了试验分析。研究结果表明:该方法将公交GPS数据与站点坐标进行匹配,能够可 靠计算出换乘判断阈值,乘客车内时间及等候时间的计算误差均小于60 s;该方法用于判别2次公交乘车是否属于换乘行为,并同时确定出换乘的站点和换乘的时间,与传统方法采用统一阈值判断换乘相比,准确性得到了大幅度的提高 。},
   ISSN = {1671-8879},
   url = {<Go to ISI>://CSCD:4911779},
   year = {2013},
   type = {Journal Article}
}

@inproceedings{
   author = {Chen, Xumei and Guo, Shuxia and Yu, Lei and Hellinga, Bruce},
   title = {Short-term Forecasting of Transit Route OD Matrix with Smart Card Data},
   booktitle = {Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems},
   series = {IEEE International Conference on Intelligent Transportation Systems-ITSC},
   pages = {1513-1518},
   note = {Times Cited: 0
ITSC
14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
OCT 05-07, 2011
Campus George Washington Univ (GWU), Washington, DC
IEEE; George Washington Univ (GWU), Sch Engn & Appl Sci; IEEE ITS Soc; Ctr Intelligent Syst Res (CISR); FHWA/NHTSA Natl Crash Anal Ctr (NCAC)},
   abstract = {This paper presents a short-term forecasting approach on transit route OD (Origin-Destination) matrix using smart card data of buses. Temporal characteristics of stop-level boardings/alightings and route-level number of passengers are analyzed. A three-step methodology that provides short-term forecasts of transit route OD matrices is proposed. The proposed method is applied to bus route 966 in Beijing as a case study. The forecasted results are compared with the observed data. The proposed short-term forecasting approach has a mean absolute percentage error no more than 13.05% or lower and a maximum absolute error no more than 17 suggesting the usefulness of the proposed method to support the deployment of dynamic urban transportation management systems.},
   ISBN = {2153-0009
978-1-4577-2197-7},
   url = {<Go to ISI>://WOS:000298654700248},
   type = {Conference Proceedings}
}

@article{
   author = {Chu, Ka Kee Alfred and Chapleau, Robert},
   title = {Augmenting Transit Trip Characterization and Travel Behavior Comprehension Multiday Location-Stamped Smart Card Transactions},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   number = {2183},
   pages = {29-40},
   note = {Times Cited: 4
0
4},
   abstract = {Trips need to be described and have always been characterized by various levels of abstraction. It varies from a simple label such as home-based work to complete itinerary with sociodemographic characteristics of the trip maker and household. The rationale behind such classifications is that planners and modelers recognize that the demand of transportation is highly differentiated. It is hoped that additional attributes would provide a more complete portrait of the demand and an improved understanding of the underlying travel behavior. Passive data collection technologies bring an extra dimension to travel data acquisition. Multiday data, which are difficult to collect, become accessible. In public transit, a smart card automatic fare collection system with automatic vehicle location capability provides high-resolution longitudinal data on travel pattern but also suffers from the inherent limitations of passive methods. This paper proposes a methodology to enhance transit trip characterization by adding a multiday dimension to a month of smart card transactions. On the basis of an individual, anchor points-precise to an exact address-are detected. Boarding and alighting locations are described with respect to those anchors. The enhancement allows in-depth travel behavior analysis on a subgroup sharing a common anchor or an individual. The paper demonstrates the use of spatial statistics, spatial analyses with geographic information system, visualizations, and data mining to describe activity space and locations and departure time dynamics, and to derive monthly trip table, activity schedule, and behavioral rules for cardholders. The results offer promising insights to transit planning and the understanding of travel behavior.},
   ISSN = {0361-1981},
   DOI = {10.3141/2183-04},
   url = {<Go to ISI>://WOS:000287296000004},
   year = {2010},
   type = {Journal Article}
}

@article{
   author = {Devillaine, Flavio and Munizaga, Marcela and Trépanier, Martin},
   title = {Detection of Activities of Public Transport Users by Analyzing Smart Card Data},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2276},
   number = {-1},
   pages = {48-55},
   ISSN = {0361-1981},
   DOI = {10.3141/2276-06},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {Du, Bowen and Yang, Yang and Lv, Weifeng},
   title = {Understand Group Travel Behaviors in an Urban Area Using Mobility Pattern Mining},
   journal = {2013 IEEE 10th International Conference on Ubiquitous Intelligence & Computing and 2013 IEEE 10th International Conference
on Autonomic & Trusted Computing},
   pages = {127-133},
   DOI = {10.1109/uic-atc.2013.64},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Farzin, Janine M.},
   title = {Constructing an Automated Bus Origin-Destination Matrix Using Farecard and Global Positioning System Data in São Paulo, Brazil},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2072},
   number = {-1},
   pages = {30-37},
   ISSN = {0361-1981},
   DOI = {10.3141/2072-04},
   year = {2008},
   type = {Journal Article}
}

@article{
   author = {Frumin, Michael and Zhao, Jinhua},
   title = {Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   number = {2274},
   pages = {52-60},
   note = {Times Cited: 2
0
2},
   abstract = {Passenger incidence (station arrival) behavior has been studied primarily to understand how changes to a transit service will affect passenger waiting times. The impact of one intervention (e.g., increasing frequency) could be overestimated when compared with another (e.g., improving reliability), depending on the assumption of incidence behavior. Understanding passenger incidence allows management decisions to be based on realistic behavioral assumptions. Earlier studies on passenger incidence chose their data samples from stations with a single service pattern such that the linking of passengers to services was straightforward. This choice of data samples simplifies the analysis but heavily limits the stations that can be studied. In any moderately complex network, many stations may have more than one service pattern. This limitation prevents the method from being systematically applied to the whole network and constrains its use in practice. This paper considers incidence behavior in stations with heterogeneous services and proposes a method for estimating incidence headway and waiting time by integrating disaggregate smartcard data with published timetables using schedule-based assignment. This method is applied to stations in the entire London Overground to demonstrate its practicality; incidence behavior varies across the network and across times of day and reflects headways and reliability. Incidence is much less timetable-dependent on the North London Line than on the other lines because of shorter headways and poorer reliability. Where incidence is timetable-dependent, passengers reduce their mean scheduled waiting time by more than 3 min compared with random incidence.},
   ISSN = {0361-1981},
   DOI = {10.3141/2274-05},
   url = {<Go to ISI>://WOS:000311649100006},
   year = {2012},
   type = {Journal Article}
}

@inproceedings{
   author = {Gong, Yongxi and Liu, Yu and Lin, Yaoyu and Yang, Jian and Duan, Zhongyuan and Li, Guicai},
   title = {Exploring spatiotemporal characteristics of intra-urban trips using metro smartcard records},
   booktitle = {Proceedings of 20th International Conference on Geoinformatics},
   publisher = {IEEE},
   pages = {1-7},
   ISBN = {1467311030},
   type = {Conference Proceedings}
}

@article{
   author = {Gordon, Jason B. and Koutsopoulos, Harilaos N. and Wilson, Nigel H. M. and Attanucci, John P.},
   title = {Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2343},
   number = {-1},
   pages = {17-24},
   ISSN = {0361-1981},
   DOI = {10.3141/2343-03},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Hickman, Mark and Noh, Hyunsoo and Lee, Sang Gu and Khani, Alireza and Nassir, Neema},
   title = {Transit Stop-Level Origin-Destination Estimation Through Use of Transit Schedule and Automated Data Collection System},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2263},
   number = {-1},
   pages = {140-150},
   ISSN = {0361-1981},
   DOI = {10.3141/2263-16},
   year = {2011},
   type = {Journal Article}
}

@inproceedings{
   author = {Hofmann, Markus and O'Mahony, Margaret},
   title = {Transfer journey identification and analyses from electronic fare collection data},
   booktitle = {Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems},
   publisher = {IEEE},
   pages = {34-39},
   ISBN = {0780392159},
   type = {Conference Proceedings}
}

@article{
   author = {Iseki, Hiroyuki and Yoh, Allison C. and Taylor, Brian D.},
   title = {Are smart cards the smart way to go? Examining their adoption by US transit agencies},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   number = {1992},
   pages = {45-53},
   note = {Times Cited: 0
0},
   abstract = {Smart card technologies offer public transit agencies unprecedented opportunity to price services flexibly by time, distance, and mode and in conjunction with neighboring transit operators. They also can simplify boarding, streamline accounting, and provide better passenger data. Some transit systems have moved quickly to adopt smart card fare media, others are moving more cautiously, and still others eschew smart cards at least for now. Why such differences? U.S. transit agencies were surveyed to gauge current levels of interest in smart card technologies as next-generation fare media; examine knowledge and perceptions of costs, benefits, and risks of smart card adoption; and determine the status of system implementation, the degree of planning toward implementation, and the levels of participation in interagency collaboration to realize interoperability. Factors common to agencies that have and have not adopted smart card technology also were examined. Results indicate that the consideration and adoption of smart card technologies and interoperable systems vary by funding availability and partnerships with other operators in joint projects for intelligent transportation systems. Perceptions of benefits, costs, and risks of such technologies also vary by status of smart card system planning and implementation. Managers often are uncertain about the costs in general and the benefits in particular of such a move, especially regarding interoperable systems. History predicts the future: technologically sophisticated operators tend to embrace smart cards, whereas those working collaboratively with other transit agencies are most likely to adopt interoperable systems.},
   ISSN = {0361-1981},
   DOI = {10.3141/1992-06},
   url = {<Go to ISI>://WOS:000252682700006},
   year = {2007},
   type = {Journal Article}
}

@article{
   author = {Jang, Wonjae},
   title = {Travel Time and Transfer Analysis Using Transit Smart Card Data},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2144},
   number = {-1},
   pages = {142-149},
   ISSN = {0361-1981},
   DOI = {10.3141/2144-16},
   year = {2010},
   type = {Journal Article}
}

@article{
   author = {Janosikova, L'udmila and Slavik, Jiri and Kohani, Michal},
   title = {Estimation of a route choice model for urban public transport using smart card data},
   journal = {Transportation Planning And Technology},
   volume = {37},
   number = {7},
   pages = {638-648},
   note = {Times Cited: 0
0},
   abstract = {This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in Zilina, the Slovak Republic, as a case study},
   ISSN = {0308-1060},
   DOI = {10.1080/03081060.2014.935570},
   url = {<Go to ISI>://WOS:000341570900004},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Kim, Kwanho and Oh, Kyuhyup and Lee, Yeong Kyu and Kim, SungHo and Jung, Jae-Yoon},
   title = {An analysis on movement patterns between zones using smart card data in subway networks},
   journal = {International Journal Of Geographical Information Science},
   volume = {28},
   number = {9},
   pages = {1781-1801},
   note = {Times Cited: 0
0},
   abstract = {Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by using an agglomerative clustering method. In addition, evaluation measures of MZPs are suggested in terms of coverage and accuracy. The effectiveness of the proposed approach is finally demonstrated through a real-world data set obtained from smart cards on a subway network in Seoul, Korea.},
   ISSN = {1365-8816},
   DOI = {10.1080/13658816.2014.898768},
   url = {<Go to ISI>://WOS:000342297000004},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Kusakabe, Takahiko and Asakura, Yasuo},
   title = {Behavioural data mining of transit smart card data: A data fusion approach},
   journal = {Transportation Research Part C-Emerging Technologies},
   volume = {46},
   pages = {179-191},
   note = {Times Cited: 0
0},
   abstract = {The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers' behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naive Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset. (C) 2014 Elsevier Ltd. All rights reserved.},
   ISSN = {0968-090X},
   DOI = {10.1016/j.trc.2014.05.012},
   url = {<Go to ISI>://WOS:000343387300011},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Lee, Sang Gu and Hickman, Mark},
   title = {Trip purpose inference using automated fare collection data},
   journal = {Public Transport},
   volume = {6},
   number = {1-2},
   pages = {1-20},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Lijun, Sun; Yang, Lu; Jian Gang, Jin; Der-Horng, Lee; Kay W. Axhausen},
   title = {An integrated Bayesian approach for passenger flow assignment in metro networks},
   journal = {Transportation Research Part C: Emerging Technologies},
   DOI = {10.1016/j.trc.2015.01.001},
   year = {2015},
   type = {Journal Article}
}

@article{
   author = {Lim, Yongtaek and Kim, Dong-Jun and Park, Jin Young},
   title = {Use of Smart Card Data to Define Public Transit Use in Seoul, South Korea},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2063},
   number = {-1},
   pages = {3-9},
   ISSN = {0361-1981},
   DOI = {10.3141/2063-01},
   year = {2008},
   type = {Journal Article}
}

@article{
   author = {Lim, Yongtaek and Kim, Hyunmyung and Limanond, Thirayoot and An, Sung-Hi},
   title = {Passenger Transfer Time in the Seoul Metropolitan Intermodal System: Can Smart-Card Data Assist in Evaluating and Improving the Transit System?},
   journal = {Ite Journal-Institute Of Transportation Engineers},
   volume = {83},
   number = {7},
   pages = {33-37},
   note = {Times Cited: 0
0},
   abstract = {This study analyzes smart-card data from the Seoul Metropolitan Transit System to obtain key transfer-time information. Smart-card data enable planners to monitor transfer-time performance and to identify specific service improvements in the system.},
   ISSN = {0162-8178},
   url = {<Go to ISI>://WOS:000321728600005},
   year = {2013},
   type = {Journal Article}
}

@inproceedings{
   author = {Liu, Liang and Hou, A. and Biderman, A. and Ratti, C.},
   title = {Understanding individual and collective mobility patterns from smart card records: A case study in Shenzhen},
   booktitle = {Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems},
   pages = {1 - 6},
   type = {Conference Proceedings}
}

@article{
   author = {Long, Y and Han, H and Yu, X},
   title = {Discovering Functional Zones Using Bus Smart Card Data and Points of Interest in Beijing. Beijing City Lab},
   journal = {Beijing City Lab. Working paper},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Long, Ying and Liu, Xingjian and Zhou, Jiangping and Gu, Yizhen},
   title = {Profiling underprivileged residents with mid-term public transit smartcard data of Beijing},
   journal = {arXiv preprint arXiv:1409.5839},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Long, Ying and Thill, Jean-Claude},
   title = {Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing},
   journal = {arXiv preprint arXiv:1309.5993},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Long, Y; Han, H; Tu, Y; Zhu X},
   title = {Evaluating the Effectiveness of Urban Growth Boundaries Using Human Mobility and Activity Records},
   journal = {Beijing City Lab. Workingpaper },
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Long, Y; Liu, X; Zhou, J; Chai, Y},
   title = {Early birds, night owls, and tireless/recurring itinerants: An exploratory analysis of extreme transit behaviors in Beijing, China},
   journal = {Beijing City Lab. Working paper},
   year = {2015},
   type = {Journal Article}
}

@article{
   author = {Luo, Lai Ping and Zhang, Jing},
   title = {Public Transportation OD Calculation Based on Trip Chain},
   journal = {Applied Mechanics and Materials},
   volume = {401-403},
   pages = {2151-2154},
   ISSN = {1662-7482},
   DOI = {10.4028/www.scientific.net/AMM.401-403.2151},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Ma, Xiaolei and Wang, Yinhai},
   title = {Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data},
   journal = {Journal of Transportation Engineering},
   volume = {140},
   number = {12},
   pages = {04014063},
   ISSN = {0733-947X
1943-5436},
   DOI = {10.1061/(asce)te.1943-5436.0000714},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Ma, Xiaolei and Wu, Yao-Jan and Wang, Yinhai and Chen, Feng and Liu, Jianfeng},
   title = {Mining smart card data for transit riders’ travel patterns},
   journal = {Transportation Research Part C: Emerging Technologies},
   volume = {36},
   pages = {1-12},
   ISSN = {0968090X},
   DOI = {10.1016/j.trc.2013.07.010},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Ma, Xiao-lei and Wang, Yin-hai and Chen, Feng and Liu, Jian-feng},
   title = {Transit smart card data mining for passenger origin information extraction},
   journal = {Journal of Zhejiang University SCIENCE C},
   volume = {13},
   number = {10},
   pages = {750-760},
   ISSN = {1869-1951
1869-196X},
   DOI = {10.1631/jzus.C12a0049},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {Medina, Sergio Arturo Ordonez and Erath, Alex},
   title = {Estimating Dynamic Workplace Capacities by Means of Public Transport Smart Card Data and Household Travel Survey in Singapore},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   number = {2344},
   pages = {20-30},
   note = {Times Cited: 0
0},
   abstract = {The number and the temporal and spatial distribution of work locations are crucial information for any transport demand model. To generate the initial transport demand of MATSim, an activity-based multiagent simulation framework, it is necessary to determine dynamic workplace capacities with high spatial resolution, either on a parcel or even a building level. Commonly applied methods to derive work locations are based on census of enterprises information, unemployment insurance database, or combined information of a building's gross floor area and individual work space requirements. As an alternative, the authors present a methodology that combines public transport smart card transaction data, travel diary surveys, and building information data sources. Work activities are detected from smart card transactions based on observed activity duration and start time and therefore related to public transport stops. To link the observed work activities to individual buildings, a linear programming optimization technique is applied that minimizes the walking time between public transport stops and potential work locations. The method classifies work activities in representative work schedules obtained by clustering methods. Information on maximum allowed building gross floor area derived from land use regulation is combined with estimates on individual work space requirements to ensure that buildings are only assigned with work activities according to their maximal capacity. To account for private transport based work activities, mode shares as observed in a travel diary are taken into account. To demonstrate the applicability, the proposed approach is implemented in Singapore and the results critically reviewed.},
   ISSN = {0361-1981},
   DOI = {10.3141/2344-03},
   url = {<Go to ISI>://WOS:000327809600004},
   year = {2013},
   type = {Journal Article}
}

@inproceedings{
   author = {Mohamed, K and Côme, Etienne and Baro, Johanna and Oukhellou, Latifa},
   title = {Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data},
   booktitle = {UrbComp},
   type = {Conference Proceedings}
}

@inproceedings{
   author = {Morency, Catherine and Trépanier, Martin and Agard, Bruno},
   title = {Analysing the variability of transit users behaviour with smart card data},
   booktitle = {Proceedings of the 9th International IEEE Conference on Intelligent Transportation Systems},
   publisher = {IEEE},
   pages = {44-49},
   ISBN = {1424400937},
   type = {Conference Proceedings}
}

@article{
   author = {Morency, Catherine and Trépanier, Martin and Agard, Bruno},
   title = {Measuring transit use variability with smart-card data},
   journal = {Transport Policy},
   volume = {14},
   number = {3},
   pages = {193-203},
   ISSN = {0967070X},
   DOI = {10.1016/j.tranpol.2007.01.001},
   year = {2007},
   type = {Journal Article}
}

@article{
   author = {Munizaga, Marcela A. and Palma, Carolina},
   title = {Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile},
   journal = {Transportation Research Part C: Emerging Technologies},
   volume = {24},
   pages = {9-18},
   ISSN = {0968090X},
   DOI = {10.1016/j.trc.2012.01.007},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {Nishiuchi, Hiroaki and King, James and Todoroki, Tomoyuki},
   title = {Spatial-Temporal Daily Frequent Trip Pattern of Public Transport Passengers Using Smart Card Data},
   journal = {International Journal of Intelligent Transportation Systems Research},
   volume = {11},
   number = {1},
   pages = {1-10},
   ISSN = {1348-8503
1868-8659},
   DOI = {10.1007/s13177-012-0051-7},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {Páez, Antonio and Trépanier, Martin and Morency, Catherine},
   title = {Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards},
   journal = {Transportation Research Part A: Policy and Practice},
   volume = {45},
   number = {7},
   pages = {640-652},
   ISSN = {09658564},
   DOI = {10.1016/j.tra.2011.04.002},
   year = {2011},
   type = {Journal Article}
}

@article{
   author = {Pelletier, Marie-Pier and Trépanier, Martin and Morency, Catherine},
   title = {Smart card data use in public transit: A literature review},
   journal = {Transportation Research Part C: Emerging Technologies},
   volume = {19},
   number = {4},
   pages = {557-568},
   ISSN = {0968090X},
   DOI = {10.1016/j.trc.2010.12.003},
   year = {2011},
   type = {Journal Article}
}

@article{
   author = {Roth, Camille and Kang, Soong Moon and Batty, Michael and Barthélemy, Marc},
   title = {Structure of urban movements: polycentric activity and entangled hierarchical flows},
   journal = {PloS one},
   volume = {6},
   number = {1},
   pages = {e15923},
   ISSN = {1932-6203},
   year = {2011},
   type = {Journal Article}
}

@article{
   author = {Song, Gao; Ying, Long},
   title = {Finding Public Transportation Community Structure based on Large-Scale Smart Card Records in Beijing},
   journal = {Beijing City Lab. Working paper},
   year = {2015},
   type = {Journal Article}
}

@article{
   author = {Spurr, Tim and Chapleau, Robert and Piche, Daniel},
   title = {Use of Subway Smart Card Transactions for the Discovery and Partial Correction of Travel Survey Bias},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   number = {2405},
   pages = {57-67},
   note = {Times Cited: 0
0},
   abstract = {Although the theoretical sources of bias in travel surveys have been documented, data that describe an entire population of travelers rarely permit the reliable detection and measurement of bias. The existence of large databases of smart card transactions in public transit systems presents an opportunity to do so. In this paper, a typical average weekday of travel demand data from the Montreal, Canada, household travel survey is confronted with a single, specific day of smart card transactions. The object of comparison is the Montreal subway system, which is involved in 10% of all daily trips within the metropolitan area. The results of the initial analysis indicate that although the survey accurately reproduces daily subway ridership, it overestimates subway boardings by 24% during peak periods. This overestimation can be corrected by adjusting the weights of home-based trips to match entry volumes at subway stations during the morning peak period. The results of the reweighting procedure suggested that francophone households that use transit had a greater propensity to respond to the survey compared with other households. Furthermore, even after reweighting, the travel survey underestimated off-peak demand by roughly 21%. The underestimation was likely attributable to underreporting of non home-based trips by respondent households and nonnsponse of specific population groups.},
   ISSN = {0361-1981},
   DOI = {10.3141/2405-08},
   url = {<Go to ISI>://WOS:000339602800009},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Sun, L. and Axhausen, K. W. and Lee, D. H. and Cebrian, M.},
   title = {Efficient detection of contagious outbreaks in massive metropolitan encounter networks},
   journal = {Scientific Reports},
   volume = {4},
   pages = {5099},
   note = {Sun, Lijun
Axhausen, Kay W
Lee, Der-Horng
Cebrian, Manuel
eng
Research Support, Non-U.S. Gov't
England
2014/06/07 06:00
Sci Rep. 2014 Jun 6;4:5099. doi: 10.1038/srep05099.},
   abstract = {Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme--a simple, but universal strategy requiring only local information--and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.},
   ISSN = {2045-2322 (Electronic)
2045-2322 (Linking)},
   DOI = {10.1038/srep05099},
   url = {http://www.ncbi.nlm.nih.gov/pubmed/24903017},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Sun, Lijun and Axhausen, Kay W and Lee, Der-Horng and Huang, Xianfeng},
   title = {Understanding metropolitan patterns of daily encounters},
   journal = {Proceedings of the National Academy of Sciences},
   volume = {110},
   number = {34},
   pages = {13774-13779},
   ISSN = {0027-8424},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {Sun, L and Jin, J G and Axhausen, K W and Lee, D H and Cebrian, M},
   title = {Quantifying long-term evolution of intra-urban spatial interactions},
   journal = {Journal of The Royal Society Interface},
   volume = {12},
   number = {102},
   pages = {20141089-20141089},
   ISSN = {1742-5689
1742-5662},
   DOI = {10.1098/rsif.2014.1089},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Sun, Lijun and Jin, Jian Gang and Lee, Der-Horng and Axhausen, Kay W. and Erath, Alexander},
   title = {Demand-driven timetable design for metro services},
   journal = {Transportation Research Part C: Emerging Technologies},
   volume = {46},
   pages = {284-299},
   ISSN = {0968090X},
   DOI = {10.1016/j.trc.2014.06.003},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Sun, Lijun and Tirachini, Alejandro and Axhausen, Kay W. and Erath, Alexander and Lee, Der-Horng},
   title = {Models of bus boarding and alighting dynamics},
   journal = {Transportation Research Part A: Policy and Practice},
   volume = {69},
   pages = {447-460},
   ISSN = {09658564},
   DOI = {10.1016/j.tra.2014.09.007},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Tao, Sui and Corcoran, Jonathan and Mateo-Babiano, Iderlina and Rohde, David},
   title = {Exploring Bus Rapid Transit passenger travel behaviour using big data},
   journal = {Applied Geography},
   volume = {53},
   pages = {90-104},
   ISSN = {01436228},
   DOI = {10.1016/j.apgeog.2014.06.008},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Tao, Sui and Rohde, David and Corcoran, Jonathan},
   title = {Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap},
   journal = {Journal of Transport Geography},
   volume = {41},
   pages = {21-36},
   ISSN = {09666923},
   DOI = {10.1016/j.jtrangeo.2014.08.006},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Trépanier, Martin and Habib, Khandker M. N. and Morency, Catherine},
   title = {Are transit users loyal? Revelations from a hazard model based on smart card data},
   journal = {Canadian Journal of Civil Engineering},
   volume = {39},
   number = {6},
   pages = {610-618},
   ISSN = {0315-1468
1208-6029},
   DOI = {10.1139/l2012-048},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {Trépanier, Martin and Morency, Catherine and Agard, Bruno},
   title = {Calculation of transit performance measures using smartcard data},
   journal = {Journal of Public Transportation},
   volume = {12},
   number = {1},
   pages = {79-96},
   ISSN = {1077-291X},
   year = {2009},
   type = {Journal Article}
}

@article{
   author = {Trépanier, Martin and Tranchant, Nicolas and Chapleau, Robert},
   title = {Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System},
   journal = {Journal of Intelligent Transportation Systems},
   volume = {11},
   number = {1},
   pages = {1-14},
   ISSN = {1547-2450},
   DOI = {10.1080/15472450601122256},
   year = {2007},
   type = {Journal Article}
}

@article{
   author = {Utsunomiya, Mariko and Attanucci, John and Wilson, Nigel},
   title = {Potential uses of transit smart card registration and transaction data to improve transit planning},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {1971},
   number = {1},
   pages = {119-126},
   ISSN = {0361-1981},
   year = {2006},
   type = {Journal Article}
}

@article{
   author = {Wang, M; Zhou, J; Long, Y},
   title = {Where to harvest social capital outside the ivory tower: Visualizing university students’ trip destinations with smartcard data.},
   journal = {Beijing City Lab. Working paper},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Wilson, Nigel H. M. and Attanucci, John and Seaborn, Catherine},
   title = {Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data},
   journal = {Transportation Research Record: Journal of the Transportation Research Board},
   volume = {2121},
   number = {-1},
   pages = {55-62},
   ISSN = {0361-1981},
   DOI = {10.3141/2121-06},
   year = {2009},
   type = {Journal Article}
}

@inproceedings{
   author = {Yuan, Nicholas Jing and Wang, Yingzi and Zhang, Fuzheng and Xie, Xing and Sun, Guangzhong},
   title = {Reconstructing individual mobility from smart card transactions: A space alignment approach},
   booktitle = {2013 IEEE 13th International Conference on Data Mining (ICDM)},
   publisher = {IEEE},
   pages = {877-886},
   ISBN = {1550-4786},
   type = {Conference Proceedings}
}

@article{
   author = {Yue, Yang and Lan, Tian and Yeh, Anthony G. O. and Li, Qing-Quan},
   title = {Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies},
   journal = {Travel Behaviour and Society},
   volume = {1},
   number = {2},
   pages = {69-78},
   ISSN = {2214367X},
   DOI = {10.1016/j.tbs.2013.12.002},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Zhang, Desheng and Huang, Jun and Li, Ye and Zhang, Fan and Xu, Chengzhong and He, Tian},
   title = {Exploring human mobility with multi-source data at extremely large metropolitan scales},
   journal = {MobiCom},
   pages = {201-212},
   DOI = {10.1145/2639108.2639116},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Zhang, Fuzheng and Yuan, Nicholas Jing and Wang, Yingzi and Xie, Xing},
   title = {Reconstructing individual mobility from smart card transactions: a collaborative space alignment approach},
   journal = {Knowledge and Information Systems},
   pages = {1-25},
   ISSN = {0219-1377},
   year = {2014},
   type = {Journal Article}
}

@inproceedings{
   author = {Zhang, Jun and Yu, Xin and Tian, Chen and Zhang, Fan and Tu, Lai and Xu, Chengzhong},
   title = {Analyzing passenger density for public bus: Inference of crowdedness and evaluation of scheduling choices},
   booktitle = {Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems},
   publisher = {IEEE},
   pages = {2015-2022},
   type = {Conference Proceedings}
}

@inproceedings{
   author = {Zhao, Juanjuan and Tian, Chen and Zhang, Fan and Xu, Chengzhong and Feng, Shengzhong},
   title = {Understanding temporal and spatial travel patterns of individual passengers by mining smart card data},
   booktitle = {Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems},
   publisher = {IEEE},
   pages = {2991-2997},
   type = {Conference Proceedings}
}

@article{
   author = {Zhong, Chen and Arisona, Stefan Müller and Huang, Xianfeng and Batty, Michael and Schmitt, Gerhard},
   title = {Detecting the dynamics of urban structure through spatial network analysis},
   journal = {International Journal of Geographical Information Science},
   volume = {28},
   number = {11},
   pages = {2178-2199},
   ISSN = {1365-8816
1362-3087},
   DOI = {10.1080/13658816.2014.914521},
   year = {2014},
   type = {Journal Article}
}

@inproceedings{
   author = {Zhou, Jiangping and Long, Ying},
   title = {Jobs–Housing Balance of Bus Commuters
in Beijing Exploration with Large-Scale Synthesized Smart Card Data},
   booktitle = {Transportation Research Board 92nd Annual Meeting},
   type = {Conference Proceedings}
}

@article{
   author = {Zhou, Jiangping and Murphy, Enda and Long, Ying},
   title = {Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data},
   journal = {Journal of Transport Geography},
   volume = {41},
   pages = {175-183},
   ISSN = {09666923},
   DOI = {10.1016/j.jtrangeo.2014.09.006},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Zhou, Jiangping and Murphy, Enda and Long, Ying},
   title = {Visualizing the minimum solution of the transportation problem of linear programming (TPLP) for Beijing&#xE2;&#x80;&#x99;s bus commuters},
   journal = {Environment and Planning A},
   volume = {46},
   number = {9},
   pages = {2051-2054},
   ISSN = {0308-518X
1472-3409},
   DOI = {10.1068/a140031g},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {Zhou, J; Wang, M; Long, Y},
   title = {Big data for intrametropolitan human movement studies: A case study of bus commuters based on smart card data},
   journal = {Beijing City Lab. Working paper},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {侯艳 and 何民 and 张生斌},
   title = {基于公交IC卡刷卡记录的居民出行OD推算方法研究},
   journal = {交通信息与安全},
   volume = {30},
   pages = {109-114},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {刘剑锋},
   title = {基于IC卡数据的城市轨道交通网络配流模型研究},
   journal = {物流技术},
   volume = {29},
   pages = {64-67},
   year = {2010},
   type = {Journal Article}
}

@article{
   author = {刘常平 and 郭继孚 and 陈金川},
   title = {北京市公共交通模型构建与应用},
   journal = {城市交通},
   volume = {6},
   pages = {19-22},
   year = {2008},
   type = {Journal Article}
}

@article{
   author = {周涛 and 翟长旭 and 高志刚},
   title = {基于公交IC卡数据的OD推算技术研究},
   journal = {城市交通},
   volume = {5},
   pages = {48-52},
   year = {2007},
   type = {Journal Article}
}

@article{
   author = {周雪梅 and 杨熙宇 and 吴晓飞},
   title = {基于IC卡信息的公交客流起止点反推方法},
   journal = {同济大学学报(自然科学版)},
   volume = {40},
   pages = {1027-1030},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {尹长勇 and 陈艳艳 and 陈绍辉},
   title = {基于聚类分析方法的公交站点客流匹配方法研究},
   journal = {交通信息与安全},
   volume = {28},
   pages = {21-24},
   year = {2010},
   type = {Journal Article}
}

@article{
   author = {张萌萌 and 郭亚娟 and 马玉娇},
   title = {基于站点吸引的公交客流 OD 分布概率模型},
   journal = {交通信息与安全},
   pages = {57-61},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {张颂 and 陈学武 and 陈峥嵘},
   title = {基于公交IC卡数据的公交站点OD矩阵推导方法},
   journal = {武汉理工大学学报：交通科学与工程版},
   pages = {333-337},
   year = {2014},
   type = {Journal Article}
}

@article{
   author = {徐建闽 and 熊文华 and 游峰},
   title = {基于GPS和IC卡的单线公交OD生成方法},
   journal = {微计算机信息},
   volume = {24},
   pages = {221-222},
   year = {2008},
   type = {Journal Article}
}

@article{
   author = {戴霄 and 陈学武},
   title = {单条公交线路的IC卡数据分析处理方法},
   journal = {城市交通},
   volume = {3},
   number = {4},
   pages = {73-76},
   year = {2005},
   type = {Journal Article}
}

@article{
   author = {戴霄 and 陈学武 and 李文勇},
   title = {公交IC卡信息处理的数据挖掘技术研究},
   journal = {交通与计算机},
   volume = {24},
   pages = {40-42},
   year = {2006},
   type = {Journal Article}
}

@article{
   author = {李海波 and 陈学武},
   title = {基于公交IC卡和AVL数据的换乘行为识别方法},
   journal = {交通运输系统工程与信息},
   volume = {13},
   pages = {73-79},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {章威 and 徐建闽},
   title = {基于GPS与IC卡的公交OD量采集方法},
   journal = {交通与计算机},
   volume = {24},
   pages = {21-23},
   year = {2006},
   type = {Journal Article}
}

@article{
   author = {胡继华 and 邓俊 and 黄泽},
   title = {结合出行链的公交IC卡乘客下车站点判断概率模型},
   journal = {交通运输系统工程与信息},
   volume = {14},
   pages = {62-67},
   year = {2014},
   type = {Journal Article}
}

@inproceedings{
   author = {薛慧, 张宇; 郑猛;},
   title = {基于CUBE与GIS的IC卡数据处理分析方法},
   booktitle = {中国城市交通规划年会},
   type = {Conference Proceedings}
}

@article{
   author = {陈君 and 杨东援},
   title = {基于智能调度数据的公交IC卡乘客上车站点判断方法},
   journal = {交通运输系统工程与信息},
   volume = {13},
   pages = {76-80},
   year = {2013},
   type = {Journal Article}
}

@article{
   author = {陈绍辉 and 陈艳艳 and 尹长勇},
   title = {基于特征站点的公交IC卡数据站点匹配方法研究},
   journal = {北京工业大学学报},
   volume = {38},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {陈绍辉 and 陈艳艳 and 赖见辉},
   title = {基于GPS与IC卡数据的公交站点匹配方法},
   journal = {公路交通科技},
   volume = {29},
   number = {5},
   pages = {102-108},
   year = {2012},
   type = {Journal Article}
}

@article{
   author = {龙瀛 and 张宇 and 崔承印},
   title = {利用公交刷卡数据分析北京职住关系和通勤出行},
   journal = {地理学报},
   volume = {67},
   number = {10},
   pages = {1339-1352},
   year = {2012},
   type = {Journal Article}
}