Inferring passenger types from commuter eigentravel matrices

ABSTRACT

A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted transportation policies. Here, a procedure is demonstrated that classifies passengers (Adult, Child/Student, and Senior Citizen) based on their three-month travel patterns. The method proceeds by constructing distinct commuter matrices, we refer to as eigentravel matrices, that capture a commuter's characteristic travel routine. Comparing various classification models, we show that the gradient boosting method gives the best prediction with 76% accuracy, 81% better than the minimum model accuracy (42%) computed using proportional chance criterion. The models are verified and validated; consequently, the procedure demonstrated should serve as a benchmark for problems of this type. The generally intuitive pattern of the demographic classification also points to a possible universal ‘travelprint’ of commuters, and can inspire development of unsupervised machine learning methods for automated fare collection systems that do not provide additional demographic detail.

KEYWORDS:

• Human mobility
• travel pattern recognition
• activity pattern recognition
• automated fare collection system
• sociodemographics
• classification
• machine learning

Acknowledgments

We would like to thank the Land Transport Authority of Singapore for the ticketing data used in this work, Nasri bin Othman for his assistance in preparing the datasets; Zhenzhou Wu and Jesus Felix Valenzuela for their valuable feedback on the work.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Erika Fille T. Legara http://orcid.org/0000-0002-5742-0415

Additional information

Funding

This research was supported by Singapore ASERC Complex Systems Programme research grant (1224504056).