Automated extraction of origin-destination demand for public transportation from smartcard data with pattern recognition

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.

Automated summary

The origin-destination travel demand matrix plays a crucial role in analyzing transportation systems, particularly in public transportation networks. This study proposes a statistical pattern recognition approach to effectively extract this matrix from passenger smartcard records, overcoming challenges like 'alighting transaction inference' and 'transfer identification'. The framework is tested on a large dataset from Melbourne's public transportation network, showing promising results in accurately estimating the demand matrix.