Avoid these 4 EDSA Southbound chokepoints

What could be causing traffic to build up in these areas? While the MMDA is currently investigating these spots, we can use the power of Google Maps to remotely check the selected areas. Let's go through each one.

The first chokepoint is an area near Balintawak bus terminal and Balintawak LRT 1. Looking at Google Maps images, we can guess some possible causes of traffic in this area. First, there's a bunch of parked cars and tricycles occupying a third of the road!

The area is also near the Balintawak Bus Terminal and the LRT station – both of which have a lot of pedestrians departing or in line, which also occupies a third of the road.

From this Google Maps image, we can see some traffic building up along the U-turn slot that's occupying two lanes.

This is the slowest choke point and perhaps the most infamous. The dip for this chokepoint is more pronounced for the AM peak, which isn't surprising since it's the intersection of two major roads which are traversed by a lot of cars.

This is a major commuting node for Metro Manila, which attracts crowds of commuters, jeepneys, PUVs, and buses especially for the weekday rush hours. Even at night, this intersection still experiences moderate traffic caused by parked PUVs (see left photo below) and lines of pedestrians (see right photo below) who have nowhere else to flag down vehicles and sometimes spill over onto the street.

For a bird's eye view of this intersection, see below for a map with annotations of other possible causes of traffic in this area.

Another intersection for two major roads, but not as bad as the first one. Could cars swerving left to take the flyover be the reason for slowdowns here?

When analyzing data, it's always smart to corroborate your results with information you already know. So how does this data match up with the MMDA's on-the-ground knowledge? Pretty well!

These choke points were among 6 EDSA problem areas that the MMDA already identified back in 2015. This gives us more confidence to use the same method to find choke points on streets where the MMDA might not have eyes and ears.

Did you notice anything interesting in the charts that we didn't see, or have you experienced any choke points aside from the 4 potential leads we listed? Feel free to send us an FB message or a tweet to share your traffic chokepoint experiences along EDSA!

Data source details and limitations

Our data source is the Waze Traffic View, a special web portal that media organizations and government agencies in the Waze Connected Citizens Program can use to monitor traffic updates and average speeds on select roads in their respective cities. Traffic view provides real-time travel speed data on user-defined monitored roads every 5 minutes.

The portal also divides each monitored road into segments (based on its traffic congestion level) and provides real-time travel speed data for each segment. However, the road segmentation is not consistently done by Waze Traffic View. To compensate for the lack of consistency, we connected the available segment-level speed data with lines, which implicitly does linear interpolation for data gaps.

One caveat regarding the precision of Waze Traffic View data: road segment length (in km) is stored up to two decimal places, so rounding errors may affect the precision of plotted points in the charts above. After removing outliers due to data collection quirks, total road length (in km) for EDSA Southbound varies from 34.25 to 34.45. (Total road length is computed by getting the sum of all road segments as collected by Traffic View.)

Because these plots use data from March 11 to April 3, 2017 only, so these are not representative of the general patterns of EDSA Southbound across all months. The analysis also doesn't consider the influence of external events such as the light truck regulation along EDSA which started last March 15, or changes in traffic caused by the rainy season or school start/end dates.

Ideally, we'd advise the MMDA and Department of Transportation to capture more of this data over a longer window of time and merge it with other transit data sources, such as the World Bank's OpenTraffic Initiative. – Rappler.com

If you want to learn how to tell data stories the Thinking Machines way, it is holding a workshop on May 3, 2017. So hurry up and register before slots run out!