Quote:
Originally Posted by ryanmaccdn
Maybe someone could bring up the Sun news article about Vancouver just slightly being behind Los Angeles when it comes to traffic congestion and travel times.
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It's worth noting that the methodology of the TomTom Congestion Index is about the relative difference in travel times between peak times and non-peak times. The
actual travel times are not factored into their index. Therefore it is of dubious quality. It's like comparing big city annual growth numbers to a small, growing city. The latter may post a spectacular percentage change year-over-year, but the former may have added an order of magnitude more people in the same period of time whilst posting an anemic low single-digit annual growth rate.
Regarding the TomTom Congestion Index, here's an example:
City A has an average commuter travelling 10 kilometres in 30 minutes during off peak hours and 60 minutes during peak periods, for a 100% TomTom Congestion Index rating.
City B has an average commuter travelling 40 kilometres in 60 minutes during off peak hours and 90 minutes during peak periods, for a 50% TomTom Congestion Index rating.
For the purposes of the TomTom Congestion Index, City A has far worse congestion than City B. However the average commuter in City A still has a commute that is only one-quarter the distance of an average commuter in City B, which saves them gas, wear and tear on their vehicles, and reduces the time that they are in jeopardy of a collision by virtue of being on the road. During times of peak congestion the average commuter in City A is on the road for 30 fewer minutes than the average commuter in City B during their respective times of peak congestion and this is an actual time savings.
A further factor that is not reflected in the index is the duration of each city's peak period of congestion. By virtue of City A's tightly knit street network of arterial roads, shorter commuting distances, and less time spent travelling by each commuter, the total peak period of congestion is likely lower than what is faced by the average commuter in City B, which has a more widely spaced street network that offers greater carrying capacity per road but imparts lower accessibility on properties and poorer network connectivity.
One final thought, the TomTom Congestion Index pulls its data from people who have TomTom GPS units. This, by definition, means that the sample used to calculate the index does not have the statistical validity of a random sample. This is because all of the data comes from those who have opted-in, creating selection bias. This is a highly specious source of data and the TomTom Congestion Index cannot be classified as a scientific study, though it presented that way by its authors and the media does not have the sophistication to know the difference. The TomTom numbers are opaque and we have no way of knowing the sample size per city, the geographic distribution of participants within the city, and the participants' frequency of use of their GPS units. I would hazard to guess that those who are travelling to and from work each day are unlikely to be using a GPS unit, as opposed to those who are required by their profession to frequently call on addresses with which they are unfamiliar. Such a road user is still affected by congestion and, therefore, may be used as a proxy for measuring the change from peak to off-peak travel times, but they are unlikely to behave in the same manner and travel the same routes as regular commuters, to whom the TomTom Congestion Index aims their findings.
Regarding my position of watching the progress of the Evergreen Line from afar, it's good to watch this thing lumber forward. I'm still unsure how the naming/interlining/Millennium line rerouting will work in practice once the Evergreen Line joins the network, but that's the very definition of a 1st World problem. We'll work something out.