Produced for the Metropolitan Manila Development Authority, November 2016
Accurate and reliable transportation data is critical for monitoring the performance of a transportation system, identifying problems, and making informed planning decisions. However, data for transportation planning is traditionally difficult to find, expensive to collect, and limited to specific locations and time periods depending on needs. Advances in transportation technologies are creating new possibilities for measuring travel conditions in urban areas at a detailed scale, both geographically and over time. Uber is committed to working together with MMDA to understand how data generated from activity on the platform in Manila can be used toward understanding travel conditions throughout the metropolitan area and improving the quality and extent of data available to MMDA when responding to needs and planning for the future. In this post we present results of an initiative to capture data from Uber trips and apply it toward gathering insights into travel conditions in Manila during the 2015 holiday season, focusing on five areas that are known to be popular origins and destinations for Manila travelers, as shown in the map below.
The examples that follow demonstrate potential ways this data can be used to gather mobility insights during a period that has historically experienced an increase in travel demand that impacts travel conditions throughout the metropolitan area. While these are basic examples, we anticipate this data can be applied toward a wide range of applications, from assessing the performance of the road network to informing traffic management strategies.
Example #1: Travel times between Timog and Makati CBD during commute hours strongly indicate an increase in traffic volume and travel time during the holidays
Increases in travel demand during the holiday season in Manila can result in additional traffic volumes and increased travel times, the impacts of which are felt throughout the network. For example, in 2010, there was an estimated 12% increase in traffic volumes along EDSA and travel times of up to 35% longer than average. While the additional load on the transportation network may be spurred in large part by seasonal travel needs, day-to-day commuters can also be impacted by worsening conditions. This example assesses week-by-week changes in travel times during commute periods in December 2015, from Timog to Makati CBD (AM Commute) and from Makati CBD to Timog (PM Commute). The week with the longest average travel times was from December 6th through the 12th, two weeks before the Christmas holiday. The week with the shortest average travel time was the week immediately following the Christmas holiday, when many workplaces may be closed.
Example #2: Travel Times to Ninoy Aquino International Airport (NAIA)
Increases in travel to the airport during the holidays can add to the overall load of the network, create additional traffic at irregular times, and contribute to delays. In 2015, reports of travelers missing their flight due to these unexpected delays on the route towards the airport, were commonplace. A big part of this can be attributed to the vast uncertainty in the time needed to reach the airport from various origin points. Travel conditions from each of the study zones to the Ninoy Aquino International Airport were assessed by time period during the month of December. Each travel time estimate was then compared to the daily average in order to understand which time periods experienced relatively long or relatively short travel times. The chart below shows travel times to the airport by zone during weekdays and weekends for the month of December. Also shown is how travel times vary from the daily average, color-coded depending on the magnitude of longer travel times than on average (red) or lower travel times (blue). Trends during December 2015 indicate that the time periods with the best conditions for travel to the airport were during the early morning (4am to 8am) or late evening (10pm to 12am). On weekdays, travel times deviated from the monthly average the most early in the morning (shorter travel times), and between 6 and 8pm (longer travel times).
Example #3: Travel Times To and From Shopping Destinations
Travel speeds in Manila during the holidays are known to be slower, with reported causes ranging from general increases in traffic volumes to jams caused by holiday sales at malls, which has lead to measures such as restricting sales to weekends only. Based on data from three major shopping destinations (Makati CBD, Ortigas Center, and North Avenue), the longest travel times in 2015 occurred during the week of December 13th, and the shortest travel times during the week of December 27th. This is consistent with the findings for weeks experiencing longest/shortest travel times in the commute hour analysis in Example #1, indicating that impacts are felt throughout the transportation system during these times. The charts below show average weekly travel times to and from key shopping areas in Manila. See the map on page 1 for reference to geographic areas included in this example.
Originally published at movement.uber.com on November 16, 2016.