Transferable supervised learning model for public transport service load estimation

Tianwei Yin (), Neema Nassir, Joseph Leong, Egemen Tanin and Majid Sarvi
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Tianwei Yin: The University of Melbourne
Neema Nassir: The University of Melbourne
Joseph Leong: The University of Melbourne
Egemen Tanin: The University of Melbourne
Majid Sarvi: The University of Melbourne

Transportation, 2025, vol. 52, issue 1, No 2, 29-54

Abstract: Abstract Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

Keywords: Passenger loads estimation; Supervised learning; Transfer learning; Missing data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11116-023-10411-2

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