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There is some discrepancy between the number of commuters in the census, and the number in the NTS. The census asks people "Where do you mainly work?", but the results are not a snapshot of an individual day. Not everyone travels to work every day (some may work from home on a given day). This could explain the discrepancy between the census commuting counts (38% of population have a commute trip), and the commuting counts in acbm (25% of population have a commute/work trip - after matching).
We use an optimisation algorithm to assign commuters to work zones in a way that minimises the difference between our assignment and the od distribution in the census. We can either
Assume that there isn't a spatial discrepancies in the proportion of people who are not going to there usual place of work on any given day. That way we can use our optimisation algorithm (use_percentages = TRUE) and the distribution will still be representative
Assume that there is a spatial discrepancy in proportion of people who are not going to there usual place of work on any given day. We can then scale down the census flows before using it in the optimisation
What dataset do we use to scale it down? We can't use the NTS as it doe not have a spatial component
The text was updated successfully, but these errors were encountered:
As presented by @sgreenbury, the discrepancy between census and acbm commuting numbers is much smaller if we don't consider work from home (wfh) trips in the census (Include category 3 only instead of 1 and 3 in classification 4a here)
One question to answer. Does the nts record wfh trips at all in the trips table? If a person starts thir day at home, works for 8 hours, and then leaves at 5pm for a grocery shop, is there any way to identify / count that work trip from the trips table. I think not - it will just show as (home - shop - home). If that is the case, we should indeed remove category 1 when doing the workzone assignment
For reference, these are the nts individuals table columns related to work from home:
WkPlace_B01ID: Work place location
IndWkGOR_B02ID: Region of usual work place
WkHome_B01ID: Work at home instead of usual workplace
PossHom_B01ID: Is it possible to work at home
OftHome_B01ID: How often do you work at home
WkMuch_B01ID: How much of your work could you do from home
PossHmN_B01ID: Is it possible work at home instead of travelling to work
There is some discrepancy between the number of commuters in the census, and the number in the NTS. The census asks people "Where do you mainly work?", but the results are not a snapshot of an individual day. Not everyone travels to work every day (some may work from home on a given day). This could explain the discrepancy between the census commuting counts (38% of population have a commute trip), and the commuting counts in acbm (25% of population have a commute/work trip - after matching).
We use an optimisation algorithm to assign commuters to work zones in a way that minimises the difference between our assignment and the od distribution in the census. We can either
use_percentages
= TRUE) and the distribution will still be representativeThe text was updated successfully, but these errors were encountered: