Thomas James Tiam-Lee

Dr. Thomas James Tiam-Lee

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Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 December 2019
Verified
Book Chapter
Open access
PDF file
KSKaoru Sumi
Kaoru Sumi
KYKeigo Yabuki
Keigo Yabuki
TTThomas James Tiam-Lee
Thomas James Tiam-Lee
ABAbdelkader Nasreddine Belkacem
Abdelkader Nasreddine Belkacem
QFQuentin Ferre
Quentin Ferre
TETeruto Endo
Teruto Endo
In this paper, we present a cooperative game, Brainio Bros 300, using a brain-computer interface (BCI). The game is cooperatively controlled by two people using P300-generating color discrimination. The two users advance through the game together, one as the “player” and the other as the “supporter” providing assistance. We assumed that players would be able-bodied, while supporters would include people with severe disabilities. Through experiments using human subjects, we evaluated the subjects’ impressions of the game and its usefulness. The results of the impression evaluation showed that the subjects generally had good impressions, and there were many opinions that such cooperative games are interesting. We also discuss the possibilities of using the P300 BCI.

19 July 2022 in European Transport Research Review
Verified
Journal Article
Open access
PDF file
TTThomas James Tiam-Lee
Thomas James Tiam-Lee
RHRui Henriques
Rui Henriques
Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

1 October 2025
Verified
Book Chapter
Closed access
PDF file
ACAndres Lucio Chan
Andres Lucio Chan
RCRalph Cedric Chua
Ralph Cedric Chua
DRDylan Del Rio
Dylan Del Rio
BLBrandon Chase Lee
Brandon Chase Lee
RORenzo Erick Ong
Renzo Erick Ong
TTThomas James Tiam-Lee
Thomas James Tiam-Lee