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Jinhuan Zhao, Adam Rahbee, N. Wilson (2007)
Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection SystemsComputer‐Aided Civil and Infrastructure Engineering, 22
Enzo Fabbiani, Sergio Nesmachnow, J. Toutouh, Andrei Tchernykh, A. Avetisyan, G. Radchenko (2018)
Analysis of Mobility Patterns for Public Transportation and Bus Stops RelocationProgramming and Computer Software, 44
Azalden Alsger, Ahmad Tavassoli, M. Mesbah, L. Ferreira (2017)
Evaluation of effects from sample-size origin-destination estimation using smart card fare data, 143
M. Bagchi, P. White (2005)
The Potential of Public Transport Smart Card DataTransport Policy, 12
W. Wang, J. Attanucci, N. Wilson (2011)
Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection SystemsThe Journal of Public Transportation, 14
Xia Zhao, Yong Zhang, Hao Liu, Shaofan Wang, Z. Qian, Yongli Hu, Baocai Yin (2019)
Detecting Pickpocketing Gangs on Buses with Smart Card DataIEEE Intelligent Transportation Systems Magazine, 11
A. Parsa, Homa Taghipour, S. Derrible, A. Mohammadian (2019)
Real-time accident detection: Coping with imbalanced data.Accident; analysis and prevention, 129
Azalden Alsger, Behrang Assemi, M. Mesbah, L. Ferreira (2016)
Validating and improving public transport origin–destination estimation algorithm using smart card fare data ☆Transportation Research Part C-emerging Technologies, 68
E. Rahimi, Ali Shamshiripour, Ramin Shabanpour, A. Mohammadian, Joshua Auld (2019)
Analysis of transit users’ waiting tolerance in response to unplanned service disruptionsTransportation Research Part D-transport and Environment, 77
Azalden Alsger, M. Mesbah, L. Ferreira, Hamid Safi (2015)
Use of Smart Card Fare Data to Estimate Public Transport Origin–Destination MatrixTransportation Research Record, 2535
M. Ilbeigi, M. Meimand (2020)
Statistical Forecasting of Bridge Deterioration ConditionsJournal of Performance of Constructed Facilities, 34
J. Barry, Robert Freimer, H. Slavin (2009)
Use of Entry-Only Automatic Fare Collection Data to Estimate Linked Transit Trips in New York CityTransportation Research Record, 2112
M. Habibian, A. Hosseinzadeh (2018)
Walkability index across trip purposesSustainable Cities and Society
Yongping Zhang, K. Martens, Ying Long (2018)
Revealing group travel behavior patterns with public transit smart card dataTravel behaviour and society, 10
M. Munizaga, C. Navarrete, Diego Silva (2013)
Validating travel behavior estimated from smartcard data
Takahiko Kusakabe, Y. Asakura (2014)
Behavioural data mining of transit smart card data: A data fusion approachTransportation Research Part C-emerging Technologies, 46
Shichang Ding, Hong Huang, Tao Zhao, Xiaoming Fu (2019)
Estimating Socioeconomic Status via Temporal-Spatial Mobility Analysis - A Case Study of Smart Card Data2019 28th International Conference on Computer Communication and Networks (ICCCN)
A. Halvorsen, H. Koutsopoulos, Zhenliang Ma, Jinhuan Zhao (2019)
Demand management of congested public transport systems: a conceptual framework and application using smart card dataTransportation
M. Munizaga, C. Palma (2012)
Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, ChileTransportation Research Part C-emerging Technologies, 24
L. Kieu, A. Bhaskar, E. Chung (2015)
A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC dataTransportation Research Part C-emerging Technologies, 58
Jason Gordon, Harilaos Koutsopoulos, N. Wilson, J. Attanucci (2013)
Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location DataTransportation Research Record, 2343
Filip Covic, S. Voß (2019)
Interoperable smart card data management in public mass transitPublic Transport, 11
Gabriel Langlois, H. Koutsopoulos, Jinhuan Zhao (2016)
Inferring patterns in the multi-week activity sequences of public transport usersTransportation Research Part C-emerging Technologies, 64
Neema Nassir, A. Khani, Sang Lee, Hyunsoo Noh, M. Hickman (2011)
Transit Stop-Level Origin–Destination Estimation through Use of Transit Schedule and Automated Data Collection SystemTransportation Research Record, 2263
M. Trépanier, N. Tranchant, R. Chapleau (2007)
Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection SystemJournal of Intelligent Transportation Systems, 11
C. Morency, M. Trépanier, B. Agard (2006)
Analysing the Variability of Transit Users Behaviour with Smart Card Data2006 IEEE Intelligent Transportation Systems Conference
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
M. Syarif, Widyawan, T. Adji (2019)
Big Data Analytics: Estimation of Destination for Users of Bus Rapid Transit (BRT) Public Transportation in Jakarta2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT)
Neema Nassir, M. Hickman, Zhenliang Ma (2015)
Activity detection and transfer identification for public transit fare card dataTransportation, 42
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.
Public Transport – Springer Journals
Published: Oct 21, 2020
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