Estimating a Transit Passenger Trip Origin-Destination Matrix Using Automatic Fare Collection System

Li, Daming; Lin, Yongjie; Xi-nan, Zhao; Song, Hongjun; Zou, Nan

doi:10.1007/978-3-642-20244-5_48

Cited by 46 publications

(25 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such as J Zhao et al [5], Cui [33], Trepanier et al [2], Barry et al [23], Nassir et al [34], Daming Li et al [21], Munizaga and Palma [25,28], António A. Nunes et al [35] and Alsger Azalden et al [30]. In the process of model application, many researchers continue to revise these assumptions in order to obtain better inferring performance.…”

Section: Trip Chaining Modelmentioning

confidence: 99%

“…In the study of smart card data, the spatio-temporal information on boarding and alighting is very important [18][19][20]. Only with these records can the above analysis be more accurate and the utilization of AFC system be more efficient [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Only several AFC systems belong to entry-exit system, which need users swiping smart card both at the beginning and the end of travel, just like AFC system of South East Queensland, Australia [4], AFC system of Seoul [27]. Hence many researchers devoted themselves to inferring missing destination information for entry-only AFC systems [2,5,21,24,[28][29][30]. However, these scholars used various methods and obtained different matching rate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

View full text Add to dashboard Cite

Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

show abstract

Section: Trip Chaining Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

View full text Add to dashboard Cite

show abstract

“…There are cases that we need to take this into account and find ways to correct possible biases in research results based on smart card data. This should not be a surprise to researchers, as smart cards' main function is collecting the fare in the transit field (Pelletier, Trepanier, & Morency, 2011) and thus smart card data could have their limitations, for instance, they do not collect information of interest to researchers such as trip length (Bagchi & White, 2005), trip destination (e.g., (Li et al, 2011)) and socio-demographics of trip makers (Long, Zhang, & Cui, 2012). Methodologies thus have to be developed and supplementary data have to be used for researchers to obtain relevant information based on smart card data.…”

Section: Smart Card Data and Human Movement Studiesmentioning

confidence: 99%

Big data for intrametropolitan human movement studies

Zhou

Wang

Long

2017

IRSPSD International

View full text Add to dashboard Cite

Abstract:Unlike the data from traditional sources, there have not been standard ways to validate the quality and reliability of information derived from big data. This article argues that the theory of urban formation can be used to do the validation. In addition, the information derived from big data can be used to verify and even extend existing theories or hypotheses of urban formation. It proposes a general framework regarding how the theory of urban formation can be employed to validate information derived from smart card data and how the validated information can supplement other data to reveal spatial patterns of economic agglomeration or human settlements. Through a case study of Beijing, it demonstrates the usefulness of the framework. Additionally, it utilizes smart card data to delineate characteristics of subcenters defined by bus commuters of Beijing.

show abstract

“…Smart card data improves strategic planning, manages the demand through the network and helps service adjustments for both short and long term (Gordillo, 2006;Alfred Chu and Chapleau, 2008;Park and Kim, 2008;Chu et al, 2009;Reddy et al, 2009;Frumin, 2010;Sun et al, 2012). As alighting information is not available form most smart card systems (due to the nature of the implemented systems), some methods were proposed using smart card data to estimate missing information such as alighting information and O-D trips (Chan, 2007;Lianfu et al, 2007;Barry et al, 2009;Kusakabe et al, 2010;Li et al, 2011;Nassir et al, 2011;Gordon et al, 2013;Costa et al, 2015;Hong et al, 2015;Nunes et al, 2015;Sun and Schonfeld, 2015;Tamblay et al, 2015).…”

Section: Smart Card Data Use In Transitmentioning

confidence: 99%

Estimation of transit origin destination matrices using smart card fare data

Alsger¹

View full text Add to dashboard Cite

Smart card fare systems have become a valuable source of information for public transport OriginDestination (O-D) estimation, allowing a better understanding of individual travel patterns and improving strategic public transport planning. The O-D matrix is important for transportation analysis, design, and management. It gives indispensable information on the travel demand between two different locations, which is used in many transportation applications from strategic planning to traffic control and management.High quality traffic information is required to improve the estimation of O-D matrix in public transportation. Many transit agencies around the world are now using smart card systems to replace traditional payment methods as a viable payment option. Furthermore, the smart card improves the quality of the data; increases the amount of statistics available; updates data continuously 24 hours per day; reduces boarding and alighting time and the driver's workload; saves costs in data collection and editing, eliminates human errors; and provides new opportunities for innovative and flexible fare structuring.Most Automated Fare Collection (AFC) systems, especially when implemented on buses, record passenger boarding information; however, no alighting information is recorded. The lack of alighting stop details is the result of the installed AFC systems, where passengers are not required to use their cards when alighting. Given the information limitations of many AFC systems, the accuracy level of the estimated O-D matrices is often unknown. In addition, many of the assumptions made in suchmethods are yet to be tested.Although smart card fare data has attracted consideration attention as a rich and comprehensive source of information, it has some limitations which constrain its application. One of these limitations is the missing information of passengers' trip purpose. The main reason for the missing information is due to its original use being the collection of revenue and not data collection. The smart card data analysed in the current research was obtained from Translink, the public transport authority of South East Queensland (SEQ), Australia. An important aspect of this system is that it includes both boarding and alighting times and locations, where a passenger gets on or off a public transport vehicle.The first stage of the current research deals with implementing, validating and improving the tripchaining method and its assumptions to estimate public transport O-D matrices using smart card fare data. Transfer time threshold, transfer walking distance, and the location of the last destination of a passenger in a given day, are the major assumptions investigated here. The available rich and unique ii smart card fare data allowed the current investigation and validation of the effect of different tripchaining method assumptions on the estimated matrices. The estimated O-D results are validated with the actual matrices, which are based on full boarding and alighting information. The errors distributi...

show abstract

Estimating a Transit Passenger Trip Origin-Destination Matrix Using Automatic Fare Collection System

Cited by 46 publications

References 7 publications

Smart Card Data Mining of Public Transport Destination: A Literature Review

Smart Card Data Mining of Public Transport Destination: A Literature Review

Big data for intrametropolitan human movement studies

Estimation of transit origin destination matrices using smart card fare data

Contact Info

Product

Resources

About