This summer I read Traffic by Tom Vanderbilt and blogged about the OR and networks topics covered in the book. The follow-up post that I promised took nearly three months longer than I thought it would.
For those of you who are familiar with driving and traffic data, you will know that men, on average, drive more than women across all ages, and it’s not even close. Men age 55+ drive more than more than twice as many miles on average as women of the same age. On face value, it seems like men are disproportionately responsible for all the traffic congestion on the road. Tom Vanderbilt challenges this idea in Traffic, looking at why and when women drive rather than looking at how much they drive.
Both men and women drive a lot more than they did in the 1950s, leading to traffic and congestion. There are many reasons for this. Two that are often named are suburban sprawl (people living farther from where they work) and women joining the workforce. This naturally led to an increase in driving by both men and women. Let’s look a bit deeper.
Other things have changed–or not–since the 1950s. One thing that has not changed is that the women who have entered the workforce still do the lion’s share of errands, particularly those involving kids (think: “soccer moms”). In the 1950s, 40% of car trips were work trips. As of ~2010, a mere 16% of car trips were work trips. The difference is not that people aren’t working or taking public transportation to work (they are actually driving to work more!). The difference is that we’ve added many other driving trips to our schedules. And women do more of these extra errands and trips than men do.
Women do a lot of “trip chaining,” stopping at the grocery store on the way to or from work, taking Johnny to soccer practice, etc. The reason why women make such an impact on congestion is because (1) they are taking these trips during peak traffic times due to inflexible schedules, (2) they they use smaller roads less equipped for large traffic loads (these trips do not usually use the interstates), and (3) the distance between trips is significant (suburban sprawl!). Side note: We women are fairly efficient here in that we can minimize travel times by “chaining” – adding a TSP-like “tour” of errands rather than making individual trips that would take longer.
I blogged about the issue of women having inflexible driving routes earlier, where I argue that dropping kids off at day care often makes taking public transportation impossible. Vanderbilt observes this, too. He also does not blame women for the extra traffic, as our travel patterns are what you would expect when considering the demands of both our families and careers. But there are implications.
In all seriousness, this post discussed traffic from the perspective of the “average” men and women. None of us are “average,” of course. The OR tie-in here is that the who, why, where, and when are important for understanding why congestion happens at certain times. The network structure is also important, as traffic network is reflected in trip chaining, and it sheds light on what parts of the network will experience the worst congestion. Vanderbilt’s writing on this topic suggests that encouraging people to contribute less to congestion is challenging, since there are many constraints on women’s driving patterns, and as a result, they might not be able to respond to incentives for reducing the amount they drive.
On a related note: for the first time in the US, the majority of licensed drivers are women.
Related posts:
Punk Rock Operations Research 
December 14th, 2012 at 12:30 pm
I think that you are saying that we can’t avoid the problems of congestion because people are making journeys that could not be made in any other way or at another time, given the pressures of schedules and work patterns. So the highways authorities have to tackle the congestion, rather than the driving habits. In my home city of Exeter, and many towns and cities in the UK (and beyond) we use a traffic management system called SCOOT (scoot-utc.com Split Cycle Offset Optimisation Technique) which uses vehicle detectors in the road to measure the traffic speed and density, and then change the traffic signals to reduce congestion.
The simplest case would be a three-way intersection, with a minor road joining a major road. The detectors would give the major road priority, and not change the lights for the secondary road until either the queue was larger than a certain quota, or the vehicle at the front of the queue had been waiting a certain length of time.
Most SCOOT systems are much larger than that. We have them on the radial roads in and out of the city, timed to take a wave of traffic through successive sets of traffic signals. (Unfortunately, on my pedal cycle, I can’t travel at the speed that SCOOT assumes, so I can’t be part of the traffic wave.)
There are numerous OR aspects to programming and implementing the system. It would make an interesting class exercise to consider what parameters to set on each control, how to measure them and review them, and how to reconcile the multiple objectives of traffic management.
As a cyclist I am concerned that the vehicle detectors should recognise the 15kg of my bicycle as a vehicle which should trigger the signals. Our city system has an override for emergency vehicles, which can be recognised automatically by the detectors in the roads, and then the signals help the vehicles to make rapid progress. One of the claimed advantages of the system is that if there is congestion which leads to traffic taking a diversion, then the system recognises the need to increase the flow along that diversion.
July 27th, 2014 at 12:51 am
[…] […]