Transportation as an information problem

In some previous posts I looked at Joe’s question about the causes of traffic and compared them to mass transit (ie rail) options. I recently returned from an excellent trip to the Bay Area–in which I found myself using BART even less than expected–and realized that I neglected to mention a very important issue when it comes to transportation decisions: information problems. In the language of social science, “information problem” is a term used to describe a situation in which a decision-maker has less information than they need to make an efficient decision*. There is a whole branch of study devoted to these issues, but here I apply them to traffic/transportation.

Before, we talked about issues of allocation: supply and demand of road space. Often however, even at high traffic times there is still road space somewhere it just happens not to be the road you are on. If you had more information about which roads were crowded and which weren’t, you could choose an alternate route. (Obviously the geographic layout of the area you’re attempting to traverse makes a difference, but in most US metro areas there are multiple viable options for getting from point A to point B.)

I’ve been debating with myself whether supply/demand or information problems are more fundamental to the issue of traffic, and haven’t reached a firm conclusion, but I can say that given the current state of transportation supply we can reach more efficient outcomes by improving access to information. There are basically three ways that I can think of to get use information in deciding your transportation means/route:

1. Habit–In situations of uncertainty, risk-averse individuals (ie most people) will often simply make the decision they made the time before. In the most drastic biological terms, this is akin to natural selection: I didn’t die last time I made this choice, so it’s probably OK this time. The problem is that being “OK” and merely surviving doesn’t get us as near to making an optimal decision. We observe people using habit quite often on daily commutes, and to some extent this is rational: they may spend more time in traffic, but they take less time to actually think about their decision. When time is valuable, though, habit is often a poor decision-maker.

2. Experience–Continuing to think about the average daily commuter, accumulated experience over multiple cycles of traffic may help to make more efficient decisions. These cycles may be daily (“if I leave at 6am there is less traffic than 8am”), weekly (in many US cities, rush hour starts earlier on Friday afternoons than it does Mon-Thurs), or even annually (think Memorial Day). The problem with experience is that it takes multiple cycles to become accustomed to patterns, and humans aren’t great at detecting patterns. On the plus side, experience can be pretty good at recognizing recurring events that don’t conform to a set time schedule, like baseball games or concerts, but is bad at detecting rare events like accidents.

3. Real-time info–Nowadays real-time traffic info is available for many major US metro areas through both city-specific services and Google Maps. In any given city I tend to think that local info (e.g. TranStar in Houston and 511 in the Bay Area for automobiles, and various iPhone apps for Metro transit in SF/DC) is better than what’s on Google Maps but it’s better than nothing if you’re in a new place. As people become more aware of these services I think they will be able to make more efficient decisions about whether and where to drive. Real-time info, alas, cannot look into the future and tell you whether traffic is getting thicker or thinner on any stretch of road at a given point in time.

The best solution is probably combining your own experience with real-time info. For example, if you see that a piece of road is not congested at 3:30pm but know that it will become clogged within the next hour, that’s better than assuming it will be traffic-free. But real-time info beats habit or experience because it can show you the results of (relatively) unpredictable events like accidents or weather.

A lot of people think that prediction is one of the main benefits of social science, and complain when it fails (for example, a spate of recent news pieces on “why didn’t political scientists predict the Arab Spring?”). I for one agree with what Marc Morjé Howard wrote recently at the Monkey Cage comparing the Arab Spring to the 1989 revolutions in Eastern Europe:

Neither set of movements was predicted—even by experts.  Although for some this may raise questions about the value of “expertise,” in my view it puts into question the importance of prediction.  Contingent events and human behavior in unknown situations are impossible to predict.  The fact that most scholars failed to predict the particular decisions made by leaders like Gorbachev, Ceausescu, Ben-Ali, or Mubarak does not necessarily mean that they did not understand the regime or society.  And it certainly does not mean we should stop studying countries, areas, and languages.  Social science still has much to offer.

I could go on but this is over 800 words so I’ll stop. This may will likely be a conversation that I pick up again in the future, so please feel free to throw in your $0.02 in the comments. 

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*This definition is not uncontroversial. My wording is mainly intended to reflect the consequences of the information problem without including scenarios described as “rational irrationality” or “limited attention.”

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