Automated extraction of origin-destination demand for public transportation from smartcard data with pattern recognition. (August 2021)

• Record Type: Journal Article
• Authors: Hamedmoghadam, Homayoun; Vu, Hai L.; Jalili, Mahdi; Saberi, Meead; Stone, Lewi; Hoogendoorn, Serge
• Is Part Of: Transportation research. Volume 129(2021)
• Publication Date: 2021-08
• Subjects: Smartcard data -- Public transportation network -- Origin–destination matrix -- Passenger travel demand -- Pattern recognition -- Destination inference -- Transfer identification -- Trip chaining; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103210
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A general framework for estimating origin–destination matrix from smartcard data. The proposed framework does not require expert knowledge on the system under study. Using statistical pattern recognition, passengers' behavior is learned from the data. Parameters which used to be determined manually are inferred analytically from the data. Patterns identified in the public transport network are consistent with actuality. Abstract: Origin-destination travel demand matrix is the signature of travel dynamics in transportation networks. Many fundamental analyses of transportation systems rely on the origin–destination demand matrix of the network. Although extraction of origin–destination travel demand for public transportation networks from ticketing data is not a new problem, yet it entails challenges, such as 'alighting transaction inference' and 'transfer identification' which are worthy of further attention. This is mainly because the state-of-the-art solutions to these challenges, are often heavily reliant on network-specific expert knowledge and extensive parameter setting, or multiple data sources. In this paper, we propose a procedure that effectively applies statistical pattern recognition techniques to address the main challenges in extracting the origin–destination demand from passenger smartcard records. Learning from patterns in the available data allows the procedure to perform well under minimum case-specific assumptions, thus it becomes applicable to smartcard data from various public transportation systems. The performance of the proposed framework is tested on a dataset of over 100 million smartcard transaction records from Melbourne's multi-modal public transportation network. Evaluations on different aspects of the proposed procedure, suggest that the identified tasks are well addressed, and the framework is able to extract an accurate estimation of the origin–destination demand matrix for the system.