By Pulse Lab Jakarta
As traffic congestion in modern cities becomes more complex and dynamic, continuous streams of data with high spatio-temporal coverage are important for transport planning to better meet the mobility needs of citizens, while safeguarding against possible harms to the environment. Whilst transportation planners in the past have relied on a range of analytical models to provide essential insights, the drawback has been with the infrequency of data from surveys and censuses on which these models depend.
With the growth in digital technologies, there are now emerging opportunities to leverage the data generated to complement existing models and approaches intended to improve transportation systems. Ride-hailing data is a promising example, as its volume and near real-time nature have the potential to inform urban planning as it relates to traffic patterns, as well as social, climate, and environmental concerns. In this blog, the team at Pulse Lab Jakarta reflects on their exploration in Bangkok, Thailand using Grab’s ride-hailing data and the research partnership they’ve forged.