There have been a few initiatives working on big data from government agencies whose responsibilities are primarily on transportation. Among them are past projects implemented by the University of the Philippines Diliman (UPD) and current projects being implemented by UPD, Ateneo de Manila University (ADMU) and De La Salle University (DLSU) with support from the Department of Science and Technology (DOST). While these projects are more oriented towards some specific objectives often linked to research & development (mandates of DOST and these academic institutions), there are still a lot of data out there that needs to be digitized, processed and analyzed. UPD has done this to some extent through its National Center for Transportation Studies (NCTS), which had been a repository for data and reports from DOTC and DPWH. However, the center does not have a funded program to undertake that repository or archival function it is expected to do. Despite much lip service from DOTC, DPWH and NEDA, no support has been extend by these agencies in the past many years.
I recently came upon this excellent work from a private firm specializing in data science. Here’s a link to one of their recent ‘stories’ showing us relevant statistics on road safety in Metro Manila:
Their website says the data set the stats and graphs are based from are from the Metropolitan Manila Development Authority (MMDA), which maintains the Metro Manila Accident Reporting and Analysis System (MMARAS). This is good work and something road safety experts can use to be able to come up with programs and projects to improve safety in Metro Manila. I hope they could also get a hand of the DPWH’s Traffic Accident Reporting and Analysis System (TARAS) data that covers national roads. Unfortunately, the DPWH has stopped encoding TARAS data recently (the PNP still collects data though) so I am not sure how recent their data set is.
We need more of such work on a lot of data our agencies are producing including data from the Land Transportation Office (LTO) and the Land Transportation Franchising and Regulatory Board (LTFRB). Such information could be used to understand our transport systems including determining how to optimise supply and demand when combined with other data sets such as geographical information systems (GIS) and socio-economic data from the census.