Posts from January 2019 (14)

When peak-hour traffic congestion gets unbearable and new roads are built, there’s an initial reduction in congestion and everyone is happy.  The congestion comes back surprisingly fast — a phenomenon known as induced demand.  Before the new roads were built, people would have been avoiding them at peak times: they might have travelled at non-peak times, or car-pooled, or taken the bus, or gone somewhere closer instead, or just made fewer trips.  With the new space, these people can now drive. That’s good for them: they must benefit from being able to drive or they would still be doing whatever they were doing before.  It’s bad news for people who were already driving in peak traffic; their new lanes are being filled up and they’ve lost most of the benefit of the new road capacity. Car unenthusiasts such as Greater Auckland (and, um, me) love to tell you all about induced demand, but even car enthusiasts will often admit it’s a thing.

On the other hand, Auckland is going through an expansion in bus services, bike paths, and near-city housing.  As more people bus, walk, and cycle, pressure on congested urban streets will decrease, as will carbon emissions from transport.  Every mass transit or active transport user is One Less Car.  Studies of short-term disruptions such as transit strikes confirm that public transport, and probably bikes, really do reduce congestion.

There’s a bit of a contradiction here, though.

If extra space on the roads provided by new construction is quickly filled up by new demand, you’d expect extra space on the roads provided by One Less Car to be filled up in the same way.  Just as the short-term congestion effects of adding or subtracting new road lanes overestimate the long-term congestion effects, the short-term congestion harms of taking away buses for a day would overestimate the long-term congestion benefits they provide.  People adapt.

For example, Seattle, in the US, has made a big effort to increase public transport in recent years, with some success. The proportion of households with fewer than two cars is increasing (in contrast to similar cities).   On the other hand, congestion (as measured by TomTom) and  vehicle miles driven are both slightly up.  The policies have been successful — there are more non-car trips than before and the stable congestion and driving statistics are for an increasing population — but congestion hasn’t decreased.  In Auckland, more people now work in or near the city and many more people get to those jobs without driving. A lot of cars have, in some sense, been taken off the roads, but congestion hasn’t decreased and motorway traffic volumes are stable.

Now, there was a recent research paper from the University of Otago (press release) looking at new cycling and walking paths in New Plymouth and Hastings, which estimated a small but persistent decrease in car use (about 1%).  But these aren’t cities where car use is strongly limited by congestion, so you wouldn’t expect much induced car traffic demand.

Even with induced demand there are real, important, benefits when people use alternatives to cars. The people who switch to bike or bus will benefit (or they wouldn’t do it). The people who weren’t previously driving in peak traffic and who now get to supply the induced demand will benefit.  Some people who would otherwise have been forced out of peak driving will be able to continue, and they, too, will benefit. But people who are in peak-hour traffic anyway don’t really benefit.  To them, it’s not One Less Car. It’s One Different Car.

Team Ratings for Round 11 Delayed Match

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 89 matches played, 70 of which were correctly predicted, a success rate of 78.7%.
Here are the predictions for last week’s games.

Predictions for Round 11 Delayed Match

Here are the predictions for Round 11 Delayed Match. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

• Dumb extrapolation watch:  An opinion piece in the NY Times says that if you gave up your smartphone for a year “you would have time to make love about 16,000 times”.  As various people including Elle Hunt worked out, that’s about 44 times per day. There are also some assumptions in there about priorities — has “sorry dear, I need to check Twitter” really replaced the canonical headache? And assumptions about definitions — “not counting foreplay“.
• From Justin Falcone on Twitter: Google Trends shows how the spelling of ‘impostor syndrome’ has changed  over the past few years
• Bad data watch: Katie Langin writes “It’s not every day that you realize you’re a data point in a scientific study—and a misrepresented data point at that. But that’s what happened to a number of current and former scientists—including me—while reading a study reporting that scientific careers have become significantly shorter in the past 50 years”
• Interesting piece in Stuff by Charlie Mitchell: “The ark, the algorithm,  and our conservation conundrum” on how species are prioritised for conservation efforts.  In particular, there’s more acknowledgement than usual that rejecting ‘algorithms’ doesn’t actually make anything better.
• Chris Knox at Herald Insights has a visualisation of holiday road deaths — in particular, the problem of New Year’s Day morning.

From Ars Technica

Some psychologists at Stanford wondered if the perception of genetic risk could actually increase people’s risk, independent of their actual genetic risk. In other words, could simply learning that you have a genetic propensity for something elicit physiological changes akin to really having that propensity, regardless of whether you have it? The team designed experiments to find out.

That is, they were looking for a placebo effect of genetic information.  It’s not a ridiculous idea that there could be one. The placebo effect is a real phenomenon (at least in some settings) and there’s no obvious reason why it should work with pills and injections but not genetic information.  And I’m in favour of the principle that giving people health information (that they didn’t ask for) is an intervention that should be evaluated like any other. However, I’m not entirely convinced.

There were two experiments. One saw that people told they had a bad-at-exercise gene variant were worse at exercise.  The other saw that people told they had a staying-hungry gene variant stayed more hungry after drinking a nutrition shake. What the story (and the research paper) makes a lot of, though, is that physiological measurements changed too. It wasn’t all in the participants’ minds (or even all in their brains).

One issue is that the evidence isn’t all that strong (especially given the publication filtering it takes to get into the media) — even though the observed differences were surprisingly large. That makes it likely more that chance contributed to the results. Also, to the extent we’re seeing random variation in exercise or in hungriness we’d expect to see the same variation in biochemical measurements. If the explanation isn’t a placebo effect, the physiological differences are exactly what you’d expect.

It’s also worth noting that the biochemical difference seen in the hunger experiment (in something called glucagon-like-peptide-1) isn’t one of the differences that have been reported for the gene in question (at least in the references given). The researchers looked for a biochemical difference that had been seen for the gene (in ghrelin), and didn’t see it.  It would have been interesting to see whether information about the hunger-related gene affected exercise capacity — if there’s something there, is it somewhat specific or is it general to being told ‘bad genes’?

Even without necessarily believing the specific conclusions of the research, though, it’s another reminder that the evidence for health benefits of most sorts of genetic information is surprisingly weak.