Congestion Pricing, Privacy, and Mesh Wireless Networking

One of the top innovators in the field of wireless networking is also one of the top innovators in sustainable transportation. And now, Zipcar founder Robin Chase brings both together to address the two biggest problems in the implementation of congestion pricing: cost and privacy.

Transportation is definitely a big producer of economically unnecessary GHGs, by which I mean the type that takes away our money without increasing our standard of living. Our use of vehicles could be cut down significantly without making us any poorer, but the right economic signals are required so that people not only don't lose money but actually make money by cutting down their emissions. One way to do this is through road pricing and congestion pricing. These are ways to encourage environmentally sensible behaviours and investments by making the use of roads, particularly at peak times, pay for the transportation infrastructure in general, but particularly to make transit investments pay off.

Congestion pricing has worked for years in Singapore, and London's experience has been positive. Those that drive finally get the benefit of less congestion and those that don't will get improvements in transit infrastructure. The downsides are that two-thirds of the money collected goes to paying the cost of the fee collection itself, and that it requires that the government keep track of who is where.

One of the reasons for the cost is that congestion pricing and toll collection tends to be based on proprietary technology. Someone has to set up separate frequencies, closed networks, proprietary protocols, and then distribute the equipment on hundreds of thousands if not millions of vehicles. In addition to that, the man must have cameras recording your whereabouts so that those without the equipment or not cooperating can be nabbed. All of this equipment is single-purpose, unless you count the benefits to the people that have other reasons for wanting to know who is where.

Instead, Chase proposes a system where the communication equipment is low-cost, standard-part hardware with open software. The users themselves would finance the major part of the hardware investment in exchange for a break on tolls. Most of the communication would be mesh networks, which is to say ad-hoc peer-to-peer networks. Since these wireless devices have relatively low power and work over low distances, like the wireless network in your house, your wireless device relays its information to my wireless device, and so on until we reach a device which is close enough to a "base station" that it can transmit it to the fixed network, and vice-versa. The bandwidth is free; I don't charge you and you don't charge me for the use of our tiny bit of the network. Oh, and everyone gets free internet access as a bonus.

The location of cars would be based on GPS and triangulation from fixed nodes. The pricing system could have more flexibility that other systems, because changing the location of cordons, or basing pricing on actual congestion, or even complex cordons with buffer zones.

Chase also proposes a locational privacy method that prevents unauthorized snoopers from tracking where you are. Essentially it works like those who pay cash rather than using credit cards. People can pre-pay the tolls and deposit untraceable (nearly) electronic tokens at the toll booth. What will be known is how much you paid for tolls, but where and when is more difficult.

The details have been revealed in individual blog posts on her Network Musings blog over the past month. It's definitely worthwhile reading.

Tags: Transportation Transportation Planning Congestion Pricing Wireless Network Privacy

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Cycling Will Kill You, But Not Cycling Will Kill You More

An interesting story in Gristmill. You are 3 times more likely to be killed on a bike than in a car. However, on a per-mile basis walking from the building to your car is even more dangerous and using public transit is 10 times safer than a car. If only the safety-conscious drivers of reinforced SUVs knew, the auto salesman in the showroom would be upselling them on a bus pass instead.

But, the article continues, the risk of death by violent collision is only one way of dying. Noncyclists are 40 percent more likely to die from a heart attack from lack of exercise. So cycling significantly reduces risk of death. On average, for every year of life lost in accidents, 20 years are gained in extra longevity.

Not even counting the others you take with you. If you die in a car crash, the odds are you are taking other people with you. If you die in a bicycle crash, you are likely the only casualty. The car or truck that hit you (or more likely some other inanimate object) may need a new coat of paint, but its occupant is likely unhurt.

Tags: Transportation Transportation Planning Cycling

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Sprawl as Linear Population Density

I've been staring at this graph for days, trying to figure out what it's telling me. The graph is from the paper "Intake fraction of nonreactive vehicle emissions in US urban areas" from Atmospheric Environment. 39 (7), by J D. Marshall, S K. Teoh, and William W. Nazaroff.

What drew me to it is a more recent paper by University of Minnesota civil engineering assistant professor Julian Marshall, that further develops some strange and beautiful mathematical properties of how cities develop. Having such a lovely straight line such as the one on the left is unusual in urban studies, knowing that each city develops in its own unique way. Each point on the graph is a city. The x axis is the population of the city as measured by the US Census. The y axis is something completely new. The paper calls it "linear population density", or the number of people living along an imaginary straight line. It is actually the population of the entire city divided by the square root of the area of the city. Since the area is measured in square metres, its square root is measured in metres, hence the unit of people per metre.

This paper stumbles upon the fact that there is a log-log linear relationship between the population of a town and its "linear population density". With the slope of the graph it means that the linear population density is proportional to p^0.59, where p is the population. Forgive my rusty math, but this tells me that you can solve for the area of the city

p^0.59 = k p a^-0.5
p^1.18 = k² p² a^-1
a = k² p^0.82

That should mean that the area of cities goes up a little bit more slowly than population, that is to say that cities get relatively more compact as they get bigger.

But in a later article, in the September 2007 issue of Urban Studies, Julian Marshall shows that in fact the historical growth path of most cities over the past 50 years is not along that line, but rather as they grow the "linear population density" remains roughly constant. Redoing my calculation above with an exponent of 0 rather than 0.59, that would mean that the area of a city grows with the square of its population. Big difference. Double the population and quadruple its size. But extrapolating a bit, and I don't know whether the model allows this, if a city starts out with a uniform population density, then as it grows it will maintain that uniform density, but if it starts out with a large gradient from high to low density, then as it expands it will maintain that gradient and sprawl more and more.

There is a limit to how the data can be used that way. This analysis is based on US census data that considers a census tract to be urban if it is over a given density threshold. So if a city sprawls quite a lot, the edges will have such low density that they won't even be considered urban at all according to the data, making the city seem smaller, and therefore less dense, than it actually is. But the Brookings Institution study "Who Sprawls Most" corrected for that and came up with similar conclusions: cities with dense cores sprawl most, and cities with more uniform densities sprawl least.

Tags: Housing Sprawl Population Density Urban Planning Smart Growth

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