Mode Split, Energy Use, and Job Location

Ever wondered if the presence of rail has much of an effect on mode choice? Yes, I'm sure it keeps you up at night too. Well, wonder no more, I think the following graph is pretty convincing that rail does have an effect:



(If you want to view this in Google Earth yourself, go here for instructions.)

The scale is hard to read, but basically the colour scale is linear, between 0% and 100% (blue to red), with deep blue being 0% of work trips by car, and deep red in areas where 100% of work trips are by car. The base data is from the 2001 Census (ABS).

Of course there is a lot more going on here. Household type varies with proximity to rail stations (in particular, there are fewer households with kids), so the above graph actually overstates the effect of the rail network, but even if you crunch the stats, you get a strong independent effect.

Another important consideration is where jobs are located. Sydney has a strong CBD, in that there are a large number of jobs in and around the CBD, and so a radial rail network provides reasonably competitive access to a large number of jobs. Job decentralisation, which some argue for, on the basis that it will reduce commute length (and energy use), is unlikely to be effective, for the following reasons

1. Any decentralized job is much more likely to be driven to, because parking will be available, and public transport will be less competitive
2. While trip length would decrease, it is easy to overestimate the size of this effect. There is plenty of evidence to suggest that substantial 'excess commuting' would continue. For more specialized jobs, decentralizing may well increase commute length, as workers are not substitutable in such cases.
3. It ignores economics. Firms locate in, or close to, the CBD for good economic reasons (access to large labour pool. agglomeration economies, etc). Trying to force jobs elsewhere (through planning) will likely impose a cost on those firms who are forced to relocate to a less desirable location.
   

FYI, the following graph shows the commute energy required for each job in the Sydney CBD. That is, it shows, for each zone, the mean number of MJ of transport energy required for workers to get to jobs in that zone. From 2001 ABS Journey to Work data. Energy is in MJ/worker of primary energy (i.e. energy in the ground).

How to view this in Google Earth at home

You need to save the following KML into a plain text file (Notepad will work), save it as whateveryoulike.kml, and then drag and drop this kml file into Google Earth:


<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://earth.google.com/kml/2.0">
<GroundOverlay>
<description>NONE</description>
<name>JTW_CARONLY_PCTbr.png</name>
<visibility>1</visibility>
<Icon>
<href>http://peter.rickwood.googlepages.com/JTW_CARONLY_PCTbr.png</href>
</Icon>
<LatLonBox id="JTW_CARONLY_PCTbr.png">
<north>-33.54141728667299</north>
<south>-34.10141604662701</south>
<east>151.3614161776215</east>
<west>150.65141715537854</west>
<rotation>0</rotation>
</LatLonBox>
</GroundOverlay>
</kml>

Petrol poverty in Sydney

Some of my work got into the popular press recently, which is nice, because, afterall, urban planning/economics is meant to be an applied field. Here is a link to the article, (sans graphics I am afraid). The article got picked up by the peak oil crowd, and you can see a couple of different versions of the graphics here and here, for example, but, for convenience, I've put a scanned version of what appeared in the Sydney Morning Herald below.

Update: Paul Krugman picked this up from the Oil Drum, and stuck it on his blog. This is higher profile than I could've expected. I guess the lesson from this is that, if you want to get exposure, it really helps to gave some pretty/garish graphics....

Here is a low-res scan of the pictures that appeared in the paper(they have been tarted up by the paper, who did a nice job on them, but they are based on maps I produced).

The graphs shows average proportion of gross household income spent on petrol (orange is > 6%, yellow 4-6%, green 2-4%, light blue 1-2%, blue < 1%). Left is with petrol at $1.5 per litre, right is with petrol at $2 per litre (assuming zero price elasticity, just to keep things simple).

A few things to note:

Firstly, because these graphs show averages, they mask a lot of variation at the household level. Some individual households in the orange zone, for example, will be spending a lot more than 6% on fuel, which others will be spending a lot less.

Secondly, these costs are petrol costs only, not vehicle purchase/depreciation, rego, tolls, or other running costs. I took a quick squiz at the latest ABS data (2004 Household Expenditure Survey) which suggests fuel costs to be roughly a third to a quarter of total running costs for cars, on average. With fuel prices up, this proportion is probably a bit out of date, and is also likely to be an underestimate for those in the outer suburbs. Nevertheless, we might expect at least an average of perhaps 15% of gross household income on car travel costs for those in the outer areas, with some households much higher. These sorts of numbers are starting to get pretty significant, and make me think that the housing affordability people should start factoring in transport costs to their measures of affordability. Even if houses on the fringe in Sydney were cheap (which they're not), the extra costs of vehicle purchase and running would make them less affordable, if they were factored in.

The graphs were compiled using a travel model calibrated on NSW Department of Transport Household Travel Survey (3000 households annually across Sydney), so thanks to them. This travel model was applied to the 2006 Census data to work out the graphs you see here. I assume 9 litres per 100km fuel efficiency, because I dont have detailed information about the spatial distribution of the vehicle fleet in Sydney.

Here are the base images I provided to the paper. They're not as pretty as the ones in the paper, unfortunately.