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Counting cars…and other vehicles as well
I saw a couple of these counting instruments set-u by the DPWH, one along Ortigas Avenue Extension and another along Sumulong Highway. These are mobile counting machines that are used to count vehicles along both directions of the roads. These are supposed to be calibrated to be able to distinguish among the various types of vehicles using the roads. I assume the classifications would be according to the types used by the DPWH and not simply a general count of the number of vehicles. I wonder though if these can accurately count smaller vehicles like motorcycles, bicycles and PMD’s like the one in the photo below.
If these can be calibrated to count bicycles and PMDs, it would allow for the establishment of baseline numbers and expansion or seasonal factors for what are now termed as micromobility modes. The recent bicycle counts done through volunteers and during peak hours cannot be expanded simply because there are no reference factors that can be used for this purpose. The available expansion or seasonal factors with DPWH derived from 24-hour counts at strategic locations along national roads only account for motor vehicles. These cover the variations of motor vehicle traffic over long periods of time (e.g., 24/7) that allows one to determine Annual Average Daily Traffic (AADT) as well as typical traffic during certain days of a week (e.g., traffic on a Monday or a Sunday).
On bike counts – Part 1
Bike counts are being conducted this June and July in many cities around the country to mainly determine the number of cyclists in various locations in the participating cities and if there was an increase in the numbers. An increase will be encouraging and should support the upgrading and expansion of the bike lanes network established during this pandemic (we are not yet in the post-Covid-19 age).
The following Images are from the Mobility Awards Facebook Page where you can find the rationale for these surveys. Vehicle or pedestrian counts are perhaps the most basic type of surveys and are conducted over certain periods of time to establish variability as well as seasonality. In the case of most if not all the counts mentioned below, surveys were and are to be conducted during the perceived peak periods for bike traffic: 6:00 to 8:00 AM and 4:00 to 6:00 PM.
Counts like these need to be evaluated or analyzed considering various contexts. While the outcomes of such counts are often presented from the point of view of advocates and there is a tendency to play with the numbers (which I will explain in more detail in succeeding posts), the bigger picture relating bike numbers to other modes of transport would be among the most important. Trip purpose is another essential factor to be considered. And there is also data on the origin-destination characteristics of trips. Historical or time series data is also important if trends are to be established.
By itself, these bicycle counts are very important data that will ultimately be useful for planning, design and construction of facilities for active transport. The lack of data on bicycles while there is a bias for motor vehicle data means we cannot see the complete picture and therefore remain car-centric when dealing with transportation issues.
Dominant trips during the day
I read this post on social media stating:
“The work commute is statistically the longest and least frequent type of journey we make in a day. Yet it dominates transport planning.Now more than ever, cities must build cycle networks to support recurring local trips: to the corner store, café, community center, or school.”
I am not sure about the context of the word “dominate” as it is used in the statement but this originates from the Dutch so perhaps there is a difference, even slight, between their case and ours. I would like to add though that aside from “going home” trips, the most dominant in the Philippine context are “to work” and “to school”. And dominant here covers frequency and distance traveled. Consequential are travel times as these are affected by the quantity and quality of facilities and services available to commuters.
I think there should also be restructuring of how surveys are conducted to capture these more frequent trips. Typical surveys like JICA’s usually ask only about the main trips during the day so those will have responses of “to work”, “to school” or “to home”. For the metro level, maybe that’s okay but at the local levels, LGUs would have to make their own surveys in order for data to support initiatives for local transport, most especially active transport. A possible starting point would be the trip chains collected that appear to be a single trips with “original origins” and “final destinations”. These can be separated or disaggregated into individual trips made by different modes rather than be defined or associated with a single (main) mode of transport. That surely would expand the data set and redefine the mode shares usually reported.