Researchers at the A*STAR Institute of High Performance Computing in Singapore have developed a machine-learning program to accurately recreate and predict public transport use based on the distribution of land use and amenities in Singapore.
The researchers collected data from the city's smartcard system on people tapping in and out of individual bus and subway stations over a period of a week, totaling more than 20 million journeys. The smartcard data then was combined with city-wide information on how land was being used and high-resolution maps that identified individual amenities within a set radius of each station.
The researchers tested three machine-learning models to find one that accurately reproduced, and then predicted, transport ridership across the city. "We found that a decision tree model performed best, with good accuracy, computational efficiency, and an easy-to-follow user display," says A*STAR researcher Christopher Monterola.
The results suggest an increase in amenities of up to 55% across the city would increase ridership. The researchers found high-resolution amenity data is a much stronger predictor of ridership than general land-use details.
From A*STAR Research
View Full Article
Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA
No entries found