Traffic is filled with these daily moments of impromptu decision making and brinksmanship. As Schelling has argued, one of the most effective, albeit risky, strategies in game theory involves the use of an “asymmetry in communication.” One driver, like Barrios Gómez in Mexico City, makes himself “unavailable” to receive messages, and thus cannot be swayed from going first through the intersection. These sorts of tactics can be quite effective, if you feel like risking your neck to prove a bit of Cold War strategy. Pedestrians, for example, are told that making eye contact is essential to crossing the street at a marked crosswalk (the kind without traffic lights), but at least one study has shown that drivers were more likely to let pedestrians cross when they did not look at the oncoming car.
Drivers at intersections are acting from a complicated set of motives and assumptions that may or may not have anything to do with traffic law. In one study, researchers showed subjects a series of photographs of an intersection toward which two vehicles, equally distant from the intersection, were traveling. One had the legal right-of-way, and the other did not; the second driver also did not know if the first driver would take the right-of-way. Subjects were asked to imagine that they were one of the drivers and to predict who would “win” the right-of-way under a variety of conditions; whether they were making eye contact, whether they were a man or a woman, and whether they were driving a truck, a medium-sized car, or a small car. Eye contact mattered hugely. When it was made, most subjects thought the driver who had the legal right-of-way would claim it. Drivers were also more likely to yield when the approaching car was the same size. They were even more likely to yield when the driver was female—an artifact, the researchers suggested, of a belief that women drivers were less “experienced,” “competent,” or “rational.” Or was it just chivalry?
Traffic is thus a living laboratory of human interaction, a place thriving with subtle displays of implied power. When a light turns green at an intersection, for example, and the car ahead of another driver has not moved, there is some chance that a horn will be sounded. But when that horn will be sounded, for how long and how many times it will be sounded, who will be sounding the horn, and who the horn will be sounded at are not entirely random variables.
These honks follow observed patterns that may or may not fit your preexisting notions. We’ve already seen that drivers in convertibles with their tops down, less cloaked in anonymity, were less likely to honk than other drivers. For a similar reason, drivers in New York City, surrounded by millions of strangers, are likely to honk more, and sooner, than a driver in a small town in Idaho, where a car that has not moved might not be a random nuisance but the stalled vehicle of a friend. What the driver ahead is doing also matters. One study showed that when a car was purposely held as the light changed to green, drivers were more likely to honk—more often and for a longer time—if the nonmoving driver was quite obviously having a cell phone conversation than if they were not. (Men, it turned out, were more likely to honk than women, though women were just as likely to visibly express anger.)
All kinds of other factors—everything from gender to class to driving experience—also come into play. In another classic American study, replicated in Australia, the status of the car that did not move was the key determinant. When the “blocking car” was “high-status,” the following drivers were less likely to honk than when a cheaper, older car was doing the blocking. A study in Munich reversed the equation, keeping the car doing the blocking the same (a Volkswagen Jetta) and looking instead at who did the honking; if you guessed Mercedes drivers were faster to the horn than Trabant drivers, you guessed right. A similar study tried in Switzerland did not find this effect, which suggests that cultural differences, like the Swiss reserve and love of quiet, may have been at work. Another study found that when the driver of the blocking car was a woman, more drivers—including women—would honk than when it was a man. An experiment in Japan found that when the blocking drivers drove cars with mandatory “novice driver” stickers, the cars behind were more likely to honk than when they did not (perhaps the horn was just a driving “lesson”). A study across several European countries found that drivers were more likely to honk, and honk sooner, when the stalled driver ahead had an identity sticker indicating that they were from another country than when they were fellow nationals.
Men honk more than women (and men and women honk more at women), people in cities honk more than people in small towns, people are more reluctant to honk at drivers in “nice” cars—perhaps you already suspected these things. The point is that as we are moving around in traffic, we are all guided by a set of strategies and beliefs, many of which we may not even recognize as we act upon them. This is one of the themes guiding a fascinating series of experiments by Ian Walker, a psychologist at the University of Bath in England. In a complex system such as traffic, Walker says, where myriad people with a loose sense of the proper traffic code are constantly interacting, people construct “mental models” to help guide them. “They just develop their own idea of how it works,” Walker told me over lunch in the village of Salisbury. “And everyone’s got different ideas.”
Take the case of a car and a bicycle at an intersection. As it happens, studies consistently show intersections to be one of the most dangerous places for cyclists (not to mention cars) in traffic. Some of the reasons have to do with visibility and other perceptual problems; these will be addressed in Chapter 3. But even when drivers do see cyclists, things are not so simple. In one study, Walker showed “drivers” (i.e., qualified drivers in a lab) a photograph of a cyclist stopped at an intersection who was gazing toward the cross street but not making a turn signal with their arm. When drivers were asked to predict the cyclist’s next move, 55 percent said the cyclist was not going to turn, but 45 percent said the opposite. “This is what I mean about the informality of people’s mental models,” he said. “There are a lot of informal signals on the road that are being used. In that study you’ve actually got half the population taking it to mean one thing and half the population taking it to mean another thing—which is crying out for accidents.”
But there’s something even more interesting than mere misinterpretation going on here, Walker suggests. In another study, Walker presented subjects (again, qualified drivers in a lab) with photographs of a brightly clad bicyclist in a number of different traffic situations in a typical English village. Using a computer, the subjects were asked to “stop” or “go” depending on what they thought the cyclist was going to do at various intersections. Cyclists were shown making a proper turn signal with the arm, giving a glance or a look over the shoulder, or not signaling at all. Results were tallied on the number of “good outcomes” (when the driver made the right choice), “false alarms” (the driver stopped when they did not have to), and what Walker predicted would be collisions. As might be expected (or hoped), drivers tended to sound false alarms most often when a cyclist looked over their shoulder or gave no signal at all. As they did not know what the cyclist was going to do, they behaved over-cautiously. But when Walker studied the “collisions,” he found that these happened most often when the cyclist had given the most clear indication of all, an arm turning signal. What’s more, when drivers made the correct decision to stop, their reaction times were slowest when they were confronted with the arm signal.
Why should proper signaling, even when it’s seen and understood by the driver, be more linked to danger in this study than lack of signaling? The answer may be that the cyclists are guilty of simply looking like humans, rather than anonymous cars. In a previous study, Walker had subjects look at various photographs of traffic and describe what was going on. When subjects saw a photograph with a car, they were more likely to refer to the photo’s subject as a thing. When subjects looked at a picture that showed a pedestrian or a cyclist, they were more likely to use language that described a person. It somehow seems natural to say “the bicyclist yielded to the car,” while it sounds strange to say “the
driver hit the bicycle.” In one photograph Walker showed, a woman was visible in a car, while a man on a bike waited behind. Although the woman could be clearly seen in the car, she was never referred to as a person, while the cyclist almost always was. Even when she was visible she was rendered invisible by the car.
In theory, this is good news for bicycle riders: What cyclist does not want to be considered human? The problem may come from the inhuman environment of traffic I have already described. Vehicles are moving at velocities for which we have no evolutionary training—for most of the life of the species we did not try to make interpersonal decisions at speed. So, when we’re driving and along comes a person on wheels, we cannot help but look at their face and, again, their eyes. In another study Walker performed, using photographs of cyclists and subjects hooked up to eye-tracking software, he found that the subjects’ gazes went instinctively toward the cyclists’ faces and lingered there longest, no matter what other information was in the picture.
Eyes are the original traffic signals. Walker has a good demonstration of this. On his laptop are two photographs of himself. In one, he is looking straight at the camera (i.e., the viewer). In another, he’s looking almost imperceptibly askance, but I could still feel, quite powerfully, that something had changed. How much had his eyes moved so that I knew he was no longer looking at me? A mere two pixels (out of 640 pixels across the width of the screen). What Walker is suggesting is that when we view a cyclist’s eyes, or even their arm motion, we begin—perhaps automatically—a chain of cognitive processing. We cannot help but look for those things we seek out when we see another person. This seems to take longer than looking at mere things, and it seems to involve more mental effort (studies have shown that electroencephalographic, or EEG, readings spike when two people’s eyes meet). We may be trying to gauge more from them than simply which direction they are going to turn. We may be looking for signs of hostility or kindness. We may be looking for reciprocal altruism. We may look where they are looking rather than see what their arm is signaling.
Whether or not we realize it, we are always making subtle adjustments in traffic. A kind of nonverbal communication is going on. Walker revealed this in a powerful way when he moved from the lab setting to the actual road. As a cyclist himself, he was curious about the anecdotal accounts from cyclists who said, in effect, that the more road space they took up, the more space passing cars gave them. He was also curious about survey reports that hinted that drivers tended to view cyclists wearing helmets as more “serious, sensible and predictable road users.”
Did any of this matter on the road, or did cars simply pass cyclists as cyclists, more or less randomly? To find out, Walker mounted a Trek hybrid bicycle with an ultrasonic distance sensor and set out on the roads of Salisbury and Bristol. He made trips wearing a helmet and not wearing a helmet. He made trips at different distances from the edge of the road. And he made trips dressed as a man and dressed as a woman, wearing, as a rough signifier of gender, a “long feminine wig.” After he had crunched the data, the numbers revealed an interesting set of patterns. The farther he rode from the edge of the road, the less space cars gave him. When he wore a helmet, vehicles tended to pass closer than when he did not wear a helmet. Passing drivers may have read the helmet as a sign that there was less risk for the cyclist if they hit him. Or perhaps the helmet dehumanized the rider. Or—and more likely, according to Walker—drivers read the helmet as a symbol of a more capable and predictable cyclist, one less likely to veer into their path. In either case, the helmet changed the behavior of passing drivers.
Finally, drivers gave Walker more space when he was dressed as a woman than as a man. Was this a “novelty effect” based on the fact there are statistically fewer female cyclists on England’s roads? Or were drivers simply thinking, “Who is this crazy man-cyclist wearing that terrible wig?” Or were drivers (whose gender Walker was not able to record) giving women cyclists more room out of some sense of politeness or, perhaps, as he suggests, because they were operating with a stereotypical idea of women cyclists as less predictable or competent?
Interestingly, the possible gender bias, however misguided, echoes the intersection study mentioned earlier, in which drivers were more likely to yield the right-of-way if a female driver was approaching. Drivers, whether aware of it or not, seem to rely on stereotypes (a version of Walker’s “mental models”). Indeed, stereotypes seem to flourish in traffic. One reason, most simply, is that we have little actual information about people in traffic, as with the “Bumper of My S.U.V.” dilemma. The second reason is that we rely on stereotypes as “mental shortcuts” to help us make sense of complex environments in which there is little time to develop subtle evaluations. This is not necessarily bad: A driver who sees a small child standing on the roadside may make a stereotypical judgment that “children have no impulse control” and assume that the child may dash out. The driver slows.
It does not take a great leap to imagine, however, the problems of seeing something that does not conform to our expectations. Consider the results of one well-known psychological study. People were read a word describing a personal attribute that confirmed, countered, or avoided gender stereotypes. They were then given a name and asked to judge whether it was male or female. People responded more quickly when the stereotypical attribute matched the name than when it did not; so people were faster to the trigger when it was “strong John” and “gentle Jane” than when it was “strong Jane” and “gentle John.” Only when subjects were actively asked to try to counter the stereotype and had a sufficiently low “cognitive constraint” (i.e., enough time) were they able to overcome these automatic responses.
Similarly, the drivers passing Walker on his bicycle seemed to be making automatic judgments. But did the stereotype of the helmet-wearing Walker as a competent, predictable cyclist help or hurt in the end? After all, motorists drove more closely to him. Would he have been better off wearing a wig, a Darth Vader mask, or anything else that sent a different “traffic signal” to the driver? The answer is unclear, but Walker came away from the experiment with a positive feeling about what looking human can mean in traffic. “You can stick a helmet on and it will lead to measurable changes in behavior. It shows that as a driver approaches a given cyclist, they can make an individual judgment on that person’s perceived needs. They are judging each person as individuals. They’re not just invoking some default behavior for passing cyclists. That’s got to be encouraging.”
Our traffic lives are ruled by anonymity, but this doesn’t mean we give up trying to infer things about the people we encounter, or acting on those things in ways we may not even register.
Waiting in Line, Waiting in Traffic:
Why the Other Lane Always Moves Faster
When people are waiting, they are bad judges of time, and every half minute seems like five.
—Jane Austen, Mansfield Park
When was the last time you were angry at something that seemed out of your control? There is a very good possibility it was in one of three situations: being stuck in a traffic jam; waiting in line at a bank, an airport, a post office, or some such place; or being placed on hold for a “customer service representative.”
In all three cases, you were in a queue. Of course, you were probably more angry in the first and third cases, because you were most likely in the privacy of your car or home. But there is ample opportunity for you to get angry in a public queue, which is why corporations have spent a lot of money, and thought long and hard, not only about how to reduce queues but how to make them feel shorter.
In traffic, we wait in several kinds of queues. Traffic lights cause the most traditional kind. The traffic light takes the place of the “server.” A particularly slow server, like a particularly slow traffic light, bears the brunt of our frustration. As with traditional queues, traffic engineers try to estimate the flow of “arrivals.” Do cars arrive in a random way, or in a “Poisson” process (after the French mathematician Si
méon-Denis Poisson), as in a bank queue? Or is it non-Poisson, nonrandom (think of immigration queues at airports, which are periodically flooded by “platoons” of deplaning passengers)? Traffic engineers extend the “cycle time” during peak hours in the same way a Starbucks might add employees during the morning rush.
There are also “moving queues,” as when you’re in the faster left-lane on a highway, stuck behind what engineers call a “platoon” of vehicles. As some vehicles shift to slower lanes, you can “move up” the queue. If someone is in your way you might flash your lights or crowd their tail, which is roughly the equivalent of lightly coughing or tapping the shoulder of someone who is daydreaming in line ahead of you and has forgotten to move. You may have noticed how we tend to do this even when it clearly will not change the overall wait time, as if the sight of empty space makes us anxious.
Traffic congestion baffles traditional queue logic. We are waiting in a queue, but we often do not know where it begins or ends. How are we to measure our progress? Whether or not traffic always acts like a traditional queue, what’s interesting is that it seems to affect us in exactly the same way. David Maister, an expert in “the psychology of queuing,” has come up with a series of propositions about waiting in line. Strikingly, they all seem to hold true for traffic.
Take proposition no. 1: “Unoccupied time feels longer than occupied time.” This is why grocery stores put magazines near the cashiers, and why we listen to radios or talk on cell phones in our cars. Or proposition no. 3: “Anxiety makes waits seem longer.” Ever been stuck in traffic on your way to an important meeting or when you were low on gas? Or proposition no. 4: “Uncertain waits are longer than known, finite waits.” This is why highway engineers use CMS, or “changeable message signs,” to tell us how long a stretch of commute will take. Studies suggest that when we know the exact time of a wait, we devote less attention to thinking about it. Traffic engineers in Delhi, India, have put up “countdown signals” on a number of traffic lights, marking the number of seconds until the light turns green, for this very reason.
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