But I think taking an economic approach can help us think about some key questions regarding cycling, such as:
- Why has cycling increased in some city centres even but declined in the suburbs?
- Why do (some) people cycle in dangerous or unpleasant conditions?
- On the other hand, why do (most) people not cycle despite the convenience and low cost?
- What are the real costs and benefits of infrastructure projects or other policies to promote cycling?
Time and money
At its simplest level (which is very much the level this post is written at, since I'm not really an economist), transport economics treats the choice of transport mode for a given journey as a function of a limited number of variables. Generally, the two most important are considered to be speed and cost. Reasonably enough, people are assumed to want to get to their destination as quickly and cheaply as possible.
The standard approach to comparing the speed of transport modes is to use door-to-door times, taking into account any time spent not going anywhere, such as sitting in traffic or waiting for a bus. In economic terms, people consider both the fixed and variable time costs of travel. The chart below illustrates this approach, showing (on the vertical axis) the estimated time taken to travel various distances (on the horizontal axis) in a typical urban environment .
Walking is the slowest mode of transport in terms of average speed (as indicated by the slope of its line), but for extremely short trips it is the fastest because there isn't any 'fixed' amount of time you have to spend getting ready, you just start walking. Cycling is the fastest mode for trips of a few kilometres despite having a lower moving speed than cars, because car users are thought to spend more time walking to and from their vehicles. Note, the average moving speed for cars in this chart is about 16km/h, which might seem very low, but that's because it's the average speed and takes into account congestion encountered in urban areas. Because bikes can filter through traffic jams cycling speed is much less affected by congestion and so is almost as quick as driving in this comparison.
This is a highly stylised comparison and you can obviously quibble with any of it, but for our purposes it makes two key points:
1. When thinking about transport speed people consider the total journey time rather than just the average moving speed.
2. On that basis, cycling may be the quickest mode for most short journeys in urban areas. Of course, if congestion is not an issue than the higher speed of cars will make it more attractive, while in extremely congested places cycling will look even better.
What about the monetary cost of travel? This is a bit more complicated, mainly due to the fixed costs involved in owning a car or bike (much less in the case of a bike). But if you set them aside and focus only only on per-trip costs, the important point for our purposes is that the cost of a bike trip is much lower than for any other mode except walking. Even if you take fixed costs into account it's likely that cycling still works out second cheapest for most people.
So if we stop at this point and consider only time and monetary costs, it looks like cycling should be an extremely attractive option for most short urban journeys. It's always cheap, and in congested traffic it's pretty quick. On this basis, cycling should be very popular in congested cities. So why isn't it?
Comfort and safety
Well, with some people it is. A growing number of Londoners, for example, choose cycling over other modes. But even in London cycling's mode share is low compared to its potential, and across the UK as a whole we still cycle very few short or medium-length trips compared to the Dutch or the Germans.
Transport economists have often acknowledged that people take other factors into account when choosing how to travel, with a key factor being the safety or comfort involved. Why 'comfort'? Because people tend to cite safety fears as a reason for not cycling, despite the 'objective' cycling casualty rates being very low even in London and the net health impact probably still positive. This suggests that it's not just the frequency of collisions that people are concerned about but also the frequency of near misses, and the generalised physical and mental discomfort of mixing with motor traffic.
Recently, some researchers have tried to understand how our own safety depends not just on our own choice of mode but also the choices of those around us. For example, in this article Anderson and Auffhammer provide evidence that people in the US are choosing bigger cars in part to protect themselves from the risks posed by everyone else's cars. The result is an 'arms race' in which the average US car becomes (a) bigger and (b) more dangerous to those around it.
Anderson and Auffhammer don't make this connection, but I think you can extend their model to cover cycling, treating bikes as basically extremely light vehicles that offer little or no protection in a collision. Viewed that way, the choice to drive rather than cycle is similar to the decision to get a bigger car - for many people it is a deviation from their preferred option, motivated by fears over their own safety, that ultimately makes the roads less safe for everyone else too. Played out over a long enough period, it's easy to see how these individual decisions can cascade into a widespread rejection of cycling due to safety fears, as opposed to just the attractiveness of other modes or changing trip patterns.
However, this dynamic won't be the same everywhere. In places where most trips are short and the alternatives are very costly or slow, cycling may still be popular. That's why cycling rates tend to be highest in the centres of big cities - even though there's a lot of vehicle traffic it tends to be slow moving, which makes it less attractive as an alternative means of transport. The mixing of vehicles and bikes, however, means that cycling casualty rates may also be higher in these areas unless safer facilities for cyclists are specifically provided.
So to summarise, cycling has a cost advantage over other modes of transport (except walking, maybe) which is relatively fixed regardless of the conditions, but its relative benefits in terms of speed and safety/comfort are highly dependent on external conditions. Cycling will be quicker than other forms of road transport if traffic is congested, but unless traffic is so congested as to be stationary then the requirement to mix with heavy traffic will also make cycling less safe.
I think this helps make sense of trends such as the fall in cycling in Outer London and the simultaneous rise in Inner London. In Outer London the increasing availability and use of cars and their relatively high speed has cancelled out much of cycling's cost advantage and decreased its safety, while in Inner London high congestion and the falling number of cars has made cycling more attractive despite a still relatively high casualty rate.
One of the benefits of setting out the cycling decision in an economic framework is that it can allows us to estimate the effects of time, cost and comfort, and that in turn should enable us to make better decisions around infrastructure. Right now TfL are really struggling to properly analyse the costs and benefits of cycling infrastructure, apparently because their models don't incorporate any estimates of mode-switching in response to infrastructure changes, so cycling schemes look poor value for money in contexts where few people currently cycle, i.e. where such schemes may be most needed.
But if we can estimate a statistical model of mode choice with three factors (time, cost and comfort), then we can get at the question of how much additional cycling we should expect to see if we change the infrastructure on a particular route in a way that changes one or more of these factors simultaneously. Such a model could be expressed as something like the following:
Pij = axij + byij+ czij
where Pij is the probability of person i choosing to cycle trip j, x is time, y is cost, z is the comfort or cycling level of service, and a, b and c are the relevant elasticities of cycling with respect to each factor. This specification accounts for the fact that infrastructure elasticities will vary by person and by trip, although you could start with some averages.
Estimating this equation from observational data would be challenging, because every trip and every person are different and because simply gathering data on how people perceive safety is so difficult. So you would probably have to start by plugging in average parameter values from existing research about attitudes to generic types of infrastructure and road layout, assumptions about average speed, and so on.
That way, you can start generating reasonable estimates of how many people might use a particular cycling facility in future, rather than just going on how many people currently cycle the same route. And that in turn means that you can get better estimates of the wider impacts of cycling, for example on how transport changes affect the 'market potential' of particular locations. This kind of analysis is already commonplace for vehicle and rail transport but not for cycling, due I think to a combination of the particular theoretical and empirical challenges posed by cycling and plain old cultural prejudice against what is still seen as an 'outsider' activity.
 Source: http://www.infrastructure.gov.au/infrastructure/mcu/urbanpolicy/files/ACTIVE_TRAVEL_DISCUSSION.pdf