Traffic

by Tom Vanderbilt

  tilt your head up: Michael Tomasello, Brian Harea, Hagen Lehmanna, and Josep Call, “Reliance on Head Versus Eyes in the Gaze Following of Great Apes and Human Infants: The Cooperative Eye Hypothesis,” Journal of Human Evolution, vol. 52, no. 3 (March 2007), pp. 314–20.

  if one does not make eye contact: Robert Wright explains this phenomenon succinctly: “When we pass a homeless person, we may feel uncomfortable about failing to help. But what really gets the conscience twinging is making eye contact and still failing to help. We don’t seem to mind not giving nearly so much as we mind being seen not giving.” (As for why we should care about the opinion of someone we’ll never encounter again: Perhaps in our ancestral environment, just about everyone encountered was someone we might well encounter again.) From The Moral Animal (New York: Alfred A. Knopf, 1994), p. 206.

  “if there are more than two”: Thomas Schelling, Choice and Consequence (Cambridge, Mass.: Harvard University Press, 1984), p. 214.

  first through the intersection: Schelling also suggested throwing one’s steering wheel out the window as a sign that one has committed to one’s course of action.

  at the oncoming car: A. Katz, D. Zaidel, and A. Elgrishi. “An Experimental Study of Driver and Pedestrian Interaction During the Crossing Conflict,” Human Factors, vol. 17, no. 5 (1975), pp. 514–27.

  Or was it just chivalry?: Jeffrey Z. Rubin, Bruce D. Steinberg, and John R. Gerrein, “How to Obtain the Right of Way: An Experimental Analysis of Behavior at Intersections,” Perceptual and Motor Skills, vol. 34 (1974), pp. 1263–74.

  in New York City: Of course, the faster pace of life in New York City also has an affect on the traffic culture. Michael Primeggia, the deputy director of the New York City Department of Transportation, told me the following joke: “What’s the shortest amount of recorded time? The time between the light turning green in New York and the horn sounding.”

  visibly express anger: Andrew R. McGarva, Matthew Ramsey, and Suzannah A. Shear, “Effects of Driver Cell-Phone Use on Driver Aggression,” Journal of Social Psychology, vol. 146, no. 2 (2006), pp. 133–46.

  replicated in Australia: S. Bochner, “Inhibition of Horn-Sounding as a Function of Frustrator’s Status and Sex: An Australian Replication and Extension of Doob and Gross,” Australian Journal of Psychology, vol. 6 (1968), pp. 194–99.

  doing the blocking: A. N. Doob and A. E. Gross, “Status of Frustrator as an Inhibitor of Horn-Honking Responses,” Journal of Social Psychology, vol. 76 (1968), pp. 213–18.

  you guessed right: Andreas Diekmann, Monika Jungbauer-Gans, Heinz Krassnig, Heinz Lorenz, and Sigrid Lorenz, “Social Status and Aggression: A Field Study Analyzed by Survival Analysis,” Journal of Social Psychology; vol. 136, no. 6 (December 1996), pp. 761–68.

  been at work: See Ben Jann, “Driver Aggression as a Function of Status Concurrence: An Analysis of Horn-Honking Responses,” Bern, Switzerland, 2002; available at www.socio.ethz.ch/de/jann. Interestingly, this study found, as in the earlier mentioned birthday study, that drivers of a vehicle were less likely to honk at a vehicle when it was of the same status. The researcher noted, however, that “our data do not reveal whether it is actually similarity which reduces aggression or rather difference that increases it.”

  when it was a man: Kay Deux, “Honking at the Intersection: A Replication and Extension,” Journal of Social Psychology, vol. 84 (1971), pp. 159–60.

  a driving “lesson”: H. Yazawa, “Effects of Inferred Social Status and a Beginning Driver’s Sticker upon Aggression of Drivers in Japan,” Psychological Reports, vol. 94 (2004), pp. 1215–20.

  from another country: The study, interestingly, found that French, Spanish, and Italian drivers were faster to the horn than German drivers (Italians were the fastest). Drivers also honked more when the visible sticker was German and not the less recognizable Australian identity sticker. See Joseph P. Forgas, “An Unobtrusive Study of Reactions to National Stereotypes in Four European Countries,” Journal of Social Psychology, vol. 99 (1976), pp. 37–42.

  suspected these things: Drivers, of course, may simply be honking in a “nonaggressive” way simply to let the driver ahead know that the light has changed. But as Dwight Hennessey has pointed out, the frequency and latency of honks indicates that more than just polite signaling is at work. See Dwight Hennessey, “The Interaction of Person and Situation Within the Driving Environment: Daily Hassles, Traffic Congestion, Driver Stress, Aggression, Vengeance and Past Performance” (Ph.D. dissertation, York University, Toronto, Ontario, April 1999).

  In another study: Ian Walker, “Signals Are Informative but Slow Down Responses When Drivers Meet Bicyclists at Road Junctions,” Accident Analysis & Prevention, vol. 37 (2005), pp. 1074–85.

  In a previous study: Ian Walker, “Road Users’ Perceptions of Other Road Users: Do Different Transport Modes Invoke Qualitatively Different Concepts in Observers?” Advances in Transportation Studies, section A, no. 6 (2005), pp. 25–32.

  rendered invisible by the car: Perhaps the subjects were distracted by simply recognizing the make and model of car. Psychologists at Vanderbilt University have shown in clinical tests that car aficionados shown pictures of cars were less able to identify faces at the same time. Car fanciers were looking at cars as if they were faces, causing a “perceptual traffic jam” in a part of the brain implicated in the “holistic” visual processes of facial recognition. See Isabel Gauthier and Kim M. Curby, “A Perceptual Traffic Jam on Highway N170: Interference Between Face and Car Expertise,” Current Directions in Psychological Science, vol. 14, no. 1 (February 2005), pp. 30–33.

  people’s eyes meet: See, for example, A. Gale, G. Spratt, AJ Chapman, and A. Smallbone, “EEG correlates of eye contact and interpersonal distance,” Biological Psychology, vol. 3, no. 4 (December 1975), pp. 237–45.

  to the actual road: For further details on the study, see Ian Walker, “Drivers Overtaking Bicyclists: Objective Data on the Effects of Riding Position, Helmet Use, Vehicle Types and Apparent Gender,” Accident Analysis & Prevention, vol. 39 (2007), pp. 417–25.

  the driver slows: There is conceivably no limit to the number and variety of stereotypes drivers possess about other vehicles and the people driving them—for example, BMW drivers are aggressive, minivan drivers are slow. How all these secret interactions all play out in traffic is virtually beyond study. Do certain car drivers act a certain way, and do we act differently toward certain cars or drivers? Do you get the finger in a Hummer and a cute smile in a Mini, and does this then affect the way you drive, which then reinforces the stereotype? Research has suggested one drawback of these stereotypes: When subjects were read the description of a crash between two cars in which the actual facts were unknown, they estimated that the speed of one car was higher when the driver was younger and in a stereotypical “boy racer” car. (The effect was even stronger when the color was red!) See Graham M. Davies and Darshana Patel, “The Influence of Car and Driver Stereotypes on Attributions of Vehicle Speed, Position on the Road and Culpability in a Road Accident Scenario,” Legal and Criminal Psychology, vol. 10, (2005), pp. 45–62.

  automatic reponses: Irene V. Blaire and Mahzarin R. Banaji, “Automatic and Controlled Processes in Stereotype Priming,” Journal of Personality and Social Psychology, vol. 70, no. 6 (1996), pp. 1142–63.

  waiting in line: See David Maister, “The Psychology of Waiting in Line,” available at http://davidmaister.com/articles/1/52/.

  on the highway itself: L., Zhang, F. Xie, and D. Levinson, “Variation of the Subjective Value of Travel Time Under Different Driving Conditions.” Paper presented at the Eighty-four Transportation Research Board Annual Meeting, January 9–13, 2005, Washington, D.C.

  groups often move faster: See David A. Hensher, “Influence of Vehicle Occupancy on the Valuation of Car Driver’s Travel Time Savings: Identifying Important Behavioural Segments,” Working Paper ITLS-WP-06-011, May 2006, Institute of Transport and Logistics Studies, University of Sydney.

  with our perception of
time: A curious example of this are the new “smart” elevator systems being installed in high-rise buildings around the world. Instead of simply calling an elevator, users are grouped according to which floor they want. In theory, this speeds up the average journey by 50 percent, but it also prompts impatience in people who see elevators bound for other floors arriving and leaving before theirs; they think they are actually waiting longer. See Clive Thompson, “Smart Elevators,” New York Times, December 10, 2006.

  “At least I’m better off than you”: See Rongrong Zhou and Dilip Soman, “Looking Back: Exploring the Psychology of Queuing and the Effect of the Number of People Behind,” Journal of Consumer Research, vol. 29 (March 2003).

  “irritated with that”: On the differences in queue systems between Wendy’s and McDonald’s, there is another factor to consider: customers’ perceptions of the length of the line. McDonald’s says that people will renege on a line that looks longer; hence it prefers shorter multiple lines, despite Wendy’s claims that a single line is faster. See “Merchants Mull the Long and Short of Lines,” Wall Street Journal, September 3, 1998.

  an eighty-minute drive: The lane-changing experiment was conducted by the CBC’s Fifth Estate. Details are available at http://www.cbc.ca/fifth/roadwarriors/research.htm.

  did passing them: Donald A. Redelmeier and Robert J. Tibshirani, “Why Cars in the Next Lane Seem to Go Faster,” Nature, vol. 35, September 2, 1999.

  at the forward roadway: See, for example, Alexei R. Tsyganov, Randy B. Machemehl, Nicholas M. Warrenchuk, and Yue Wang, “Before-After Comparison of Edgeline Effects on Rural Two-Lane Highways,” Report No. FHWA/TX-07/0-50902 (Austin: Center for Transportation Research, University of Texas at Austin, 2006).

  stay in our lane: See, for example, D. Salvucci, A. Liu, and E. R. Boer, “Control and Monitoring During Lane Changes,” in Vision in Vehicles: 9, conference proceedings (Brisbane, Australia, 2001).

  looking in the rearview mirror: The forward and rearview percentages are drawn from M. A. Brackstone and B. J. Waterson, “Are We Looking Where We Are Going? An Exploratory Examination of Eye Movement in High Speed Driving.” Paper 04-2602, Proceedings of the 83rd Annual Meeting of the Transportation Research Board (Washington D.C., January 2004).

  “loss aversion”: The notion of loss aversion was first hypothesized by Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica, vol. 47 (1979), pp. 263–91.

  sensitive to loss: See Sabrina M. Tom, Craig R. Fox, Christopher Trepel, and Russell A. Poldrack, “The Neural Basis of Loss Aversion in Decision-Making Under Risk,” Science, vol. 315, no. 5811 (26 January 2007), pp. 515–18. See also William J. Gehring and Adrian R. Willoughby, “The Medial Frontal Cortex and the Rapid Processing of Monetary Gains and Losses,” Science, vol. 295, no. 5563 (2002), pp. 2279–82.

  “endowment effect”: D. Kanheman, J. L. Knetsch, and R. H. Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem,” Journal of Political Economy, vol. 98 (1990) pp. 1325–48.

  to the person leaving it: The parking lot studies were chronicled in R. Barry Ruback and Daniel Juieng, “Territorial Defense in Parking Lots: Retaliation Against Waiting Drivers,” Journal of Applied Social Psychology, vol. 27, no. 9 (1997), pp. 821–34. The authors suggest another theory: that fighting for the “symbolic value” of the parking space when it is threatened by an intruder helps give the parking spot owner a feeling of heightened control over the situation. This is why, they suggest, people will take even longer to vacate a spot when the waiting driver honks. It is a threat to their “sense of freedom,” and the best response is to simply stay longer in the parking space, thus asserting that sense of freedom.

  involved lane changes: Basav Sen, John D. Smith, and Wassim G. Najm, “Analysis of Lane Change Crashes,” DOT-VNTSC-NHTSA-02-03, National Highway Traffic Safety Administration, March 2003.

  how many were discretionary?: One study that compared crashes to traffic volume (obtained via loop-inductor data) found that most lane-change crashes occurred, perhaps not surprisingly, when the variability of highway speeds across lanes was highest—in other words, the time when most people would find it advantageous to change lanes. See Thomas F. Golob, Wilfred W. Recker, and Veronica M. Alvarez, “Freeway Safety as a Function of Traffic Flow,” Accident Analysis & Prevention, vol. 36 (2004), pp. 933–46.

  decisions we make while driving: At Cooper University Hospital in New Jersey, for example, doctors estimate that 60 percent of the trauma intensive care unit patients are the victims of car crashes; see Geoff Mulvihill, “In Corzine’s Hospital Unit, Handling Terrible Accidents Routine,” Newsday, April 23, 2007.

  work zones: The work-zone fatality statistic comes from the U.S. Federal Highway Administration (http://safety.fhwa.dot.gov/wz/wz_facts.htm).

  “merging difficulties”: From Understanding Road Rage: Implementation Plan for Promising Mitigation Measures, by Carol H. Walters and Scott A. Cooner (Texas Transportation Institute, November 2001).

  lane that will close: Information on work-zone merge strategies was drawn from a number of useful sources, including “Dynamic Late Merge Control Concept for Work Zones on Rural Freeways,” by Patrick T. McCoy and Geza Pesti, Department of Civil Engineering, University of Nebraska.

  smoothly through the work zone: The TRL data comes from a report by G. A. Coe, I. J. Burrow, and J. E. Collins, “Trials of ‘Merge in Turn’ Signs at Major Roadworks.” Unpublished project report, PR/TT/043/95, N207, October 30, 1997.

  exactly where to merge: For a sample discussion of U.K. merging ambiguity, see http://www.pistonheads.com/gassing/topic.asp?f=154&h=&t=256729 Retrieved on December 1, 2007.

  which is also safer: See Federal Highway Administration, U.S. Department of Transportation, “Methods and Procedures to Reduce Motorist Delays in European Work Zones,” FHWA-PL-00-001, October 2000.

  One important caveat: Another simulation study showed that the Late Merge strategy was more effective when two lanes narrowed to one than when three narrowed to two. According to one report, “A possible explanation may be evident in the way vehicles appeared to be behaving in the simulations. When simulation animations of the 3-to-2 lane configurations of the late merge control were viewed, it appeared that vehicles driving in the middle lane would move to the far left lane to avoid merging from the closing lane. This interaction slowed vehicles in the far left lane enough that throughput may have been significantly reduced.” Evaluation of the Late Merge Workzone Traffic Control Strategy, by Andrew G. Beacher, Michael D. Fontaine, and Nicholas J. Garber. Virginia Transportation Research Council, August 2004, VTRC 05—R6.

  summer of 2003: The Minnesota Dynamic Late Merge information was drawn from two reports, “Dynamic Late Merge System Evaluation: Initial Deployment on I-10,” prepared by URS for the Minnesota Department of Transportation,” and a follow-up study, “Evaluation of 2004 Dynamic Late Merge System for the Minnesota Department of Transportation,” also prepared by URS.

  blocked by trucks: Garber, in a telephone conversation, also noted the particular tendency of trucks to perform blocking maneuvers. He found that Late Merge worked best when the total volume of heavy vehicles in the traffic stream was less than 20 percent.

  Chapter Two: You’re Not as Good a Driver as You Think You Are

  fifteen hundred “subskills”: This estimate comes from A. J. McKnight and B. Adams, Driver Education Task Analysis, vol. 1, Task Descriptions, Washington D.C.: National Highway Traffic Safety Administration, 1970.

  twenty per mile: Leslie George Norman, “Road Traffic Accidents: Epidemiology, Control and Prevention” (World Health Organization, Public Health Papers no. 12, 1962), p. 51.

  440 words, per minute: This figure comes from William Ewald, Street Graphics (Washington, D.C.: American Society of Landscape Architects Foundation), p. 32.

  “avoiding obstacles”: See Urban Challenge Rules (Arlington, Va.: Defense Advanced Research Projects Agency, July 10, 2007).

 
; in the future: The cognitive scientist Donald D. Hoffman points out that an average traffic scene of a tree-lined street with cars creates a multitude of problems for computer intelligence, as analysis by researcher Scott Richman has revealed. Hoffman notes, “Several problems that Richman faced are evident from this picture: clutter, trees moving in the wind, shadows dancing on the road, cars in front hiding cars behind. A sophisticated analysis of motion, using several frames of motion at once, allows Richman’s system to distinguish the motion of cars from that of trees and shadows…. [Richman’s] system can trackcars through shadows, a feat that is trivial for our visual intelligence but, heretofore, quite difficult for computer vision systems. It’s easy to underestimate our sophistication at constructing visual motion. That is, until we try to duplicate that sophistication on a computer. Then it seems impossible to overestimate it.” From Donald D. Hoffman, Visual Intelligence (New York: W. W. Nortion, 1998), p. 170.

  “caution for the caution”: See, for example, Don Leavitt, “Insights at the Intersection,” Traffic Management and Engineering, October 2003.

  sooner than necessary: H. Kölla, M. Badera, and K. W. Axhausen, “Driver Behavior During Flashing Green Before Amber: A Comparative Study,” Accident Analysis & Prevention, vol. 36, no. 2 (March 2004), pp. 273–80.

  without the flashing green: D. Mahalel and D. M. Zaidel, “Safety Evaluation of a Flashing Green Light in a Traffic Signal,” Traffic Engineering and Control, vol. 26, no. 2 (1985), pp. 79–81.

  chances to crash: This point is made in L. Staplin, K. W. Gish, L. E. Decina, K. H. Lococo, D. L. Harkey, M. S. Tarawneh, R. Lyles, D. Mace, and P. Garvey in Synthesis of Human Factors Research on Older Drivers and Highway Safety, vol. 2, Publication No. FHWA-RD-97-095, 1997. Available at http://www.fhwa.dot.gov/tfhrc/safety/pubs/97094/97094.htm.

  “bump itself up the queue”: One might think that robot drivers would be free from the complicated psychological dynamics that trouble humans at intersections; yet, perhaps like humans, it all depends on how they are wired. “Robots can be more aggressive or more conservative,” Montemerlo told me. You might, for example, “program your robot to always ignore the queuing order and always go first, to be a pushy robot.” But whether or not this strategy works depends on how the other robots have been programmed. Four pushy robots at a four-way stop could get ugly quickly.