When I was a naive young modeler, running the Travel and Travel/2 models for the Montgomery County Planning Departments, regional travel demand models took up to 24 hours to run in full form. Talking with modelers today, it seems models still take on the order of 24 hours to run. Why?
I posit “Induced Complexity.” When we build a road, we induce demand, travelers who were previously priced off the road due to congestion or extra travel time now switch times of day, routes, modes, and destinations to take advantage of the capacity, and new development is pursued. Similarly, when we get a bigger computer, we can either use it to run the same models faster, or to run more complicated models. It seems the profession leans to the latter. The complexity is in terms of the number of Transportation Analysis Zones, or in the number of Times of Day, or in the number of model components that are considered, or the degree of precision required in equilibrium.
This induced complexity is real, and like induced demand is not necessarily a bad thing (if the complexity improves accuracy, it is a good thing), but it is a thing we should all be cognizant of.
From the San Francisco papers a while back, I saw a headline “”City rids streets of hundreds of garbage cans: Mayor says high number led to trash overflows””
An article about this: Trash cans cut back on city streets / Mayor defends policy but supervisors, residents complain
On its face, eliminating garbage cans will not eliminate garbage, so what is the mental model Mayor Newsom has?
(a) by increasing the transportation cost of disposal, people will create less waste? (The induced demand argument.
(b) people/businesses are free-riding on public trash receptacles, and that by cutting back, people will fund their own receptacles?
The question needs to be asked why were public trash receptacles initially deployed? One suspects public dumping of waste and littering were problems, otherwise a solution would never have been proposed. Public dumping and littering are not mere aesthetic issues, there is also a significant public health problem. To sustain a large population in a small area, waste must be managed.
The example of Amsterdam may be worth visiting. Receptacles there are port-holes into a much large waste storage dumpster under the ground that is cleared every morning by giant mechanical cleaning machines in a fascinating example of advanced technology for seemingly mundane uses. This applies to recycling as well.
Four pictures I took in Amsterdam of waste collection in 2003
Network growth is a complex phenomenon. Some have suggested that it occurs in an orderly or rational way, based on the size of the places that are connected. David Levinson examines the order in which stations were added to the London surface rail and Underground rail networks in the nineteenth and twentieth centuries, testing the extent to which order correlates with population density. While population density is an important factor in explaining order, he shows that other factors were at work. The network itself helps to reshape land uses, and a network that may have been well ordered at one time may drift away from order as activities relocate.
This article examines the changes that occurred in the rail network and density of population in London during the 19th and 20th centuries. It aims to disentangle the ‘chicken and egg’ problem of which came first, network or land development, through a set of statistical analyses clearly distinguishing events by order. Using panel data representing the 33 boroughs of London over each decade from 1871 to 2001, the research finds that there is a positive feedback effect between population density and network density. Additional rail stations (either Underground or surface) are positive factors leading to subsequent increases in population in the suburbs of London, while additional population density is a factor in subsequently deploying more rail. These effects differ in central London, where the additional accessibility produced by rail led to commercial development and concomitant depopulation. There are also differences in the effects associated with surface rail stations and Underground stations, as the Underground was able to get into central London in a way that surface rail could not. However, the two networks were weak (and statistically insignificant) substitutes for each other in the suburbs, while the density of surface rail stations was a complement to the Underground in the center, though not vice versa.
Perhaps more interesting for the non-academic, we (Ahmed El-Geneidy, Feng Xie, and myself of the Nexus group) have put together three quicktime movies
1.The co-evolution of London population density and surface (National) rail
2.The co-evolution of London population density and the Underground
3.The co-evolution of London population density and surface (National) rail and the Underground