Traffic (50 page)

nowhere near critical density: “Possible Explanations of Phase Transitions in Highway Traffic,” C. F. Daganzo, M. J. Cassidy, and R. L. Bertini, Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, Berkeley, May 25, 1998.

If done properly: This is not to say that ramp meters always work perfectly, because nothing in traffic is ever so easy. Timing patterns may be skewed (although this is being addressed with real-time, system-wide adaptive ramp meters). Ramp metering done without carefully studying the traffic terrain can lead to “perverse outcomes,” one study suggests, as in the case of metered on-ramp drivers being held hostage by a “downstream” off-ramp they will not even use (congestion caused “not by too many cars getting on the freeway but by too many cars trying to get off”). Too many cars held on the ramp, no matter how desirable for the freeway, can back up into local streets, triggering other jams. Needless to say, for metering to work properly, people actually need to obey the signals. There is a fairness issue as well, as the authors of the Minnesota study pointed out: Ramp metering favors those making longer trips and actually hurts those traveling only a few exits. See Michael Cassidy, “Complications at Off-Ramps,”
Access
magazine, January 2003, pp. 27–31.

one-third less time: The rice experiment (proposed by Paul Haase) was the winning entry in a contest sponsored by the Washington DOT for the best way to visualize “throughput maximization” Susan Gilmore, “Rice Is Nice When Trying to Visualize Highway Traffic,”
Seattle Times,
December 29, 2006.

“like cars on the highway”: To wit: “Traffic flow resembles granular flow nowhere more closely than on the highway. Here the individual behavior of the drivers forms a relatively small statistical perturbation on the deterministic part of the collective motion, and hence the cars can be treated as physical particles. Both are many particle systems far from equilibrium, in which the constant competition between driving forces and dissipative interactions leads to self-organized structures: Indeed, there is a strong analogy between the formation of traffic jams on the highway and the formation of particle clusters in a granular gas.” From K. van der Weele, W. Spit, T. Mekkes, and D. van der Meer, “From Granular Flux Model to Traffic Flow Description,” in
Traffic and Granular Flow 2003,
eds. S. P. Hoogendoorn, S. Luding, P. H. L. Bovy, M. Schreckenberg, and D. E. Wolf (Berlin: Springer, 2005), pp. 569–78. On the other hand, G. F. Newell, a seminal traffic flow researcher, once cautioned that “some researchers try to associate with vehicular traffic all sorts of phantom phenomena analogous to the effects in gases. They don’t exist.” G. F. Newell, “Memoirs on Highway Traffic Flow Theory in the 1950s,”
Operations Research,
vol. 50, no. 1 (January–February 2002), pp. 173–78.

“through the hopper”: Rice is not a perfect metaphor for traffic either. As Benjamin Coifman points out, “Traffic is mostly a one-dimensional system within the lane, with occasional coupling to adjacent lanes. Traditional granular flow is three-dimensional. And then in traffic you are dealing with smart particles.” (Author interview.)

between the grains: The German physicist and traffic researcher Dirk Helbing has observed a similar phenomenon at work in the “outflow” of people from crowded rooms. “Panicking pedestrians often come so close to each other, that their physical contacts lead to the buildup of pressure and obstructing friction effects.” This can occur even when the exits are fairly wide. Why? “This comes from disturbances due to pedestrians, who expand in the wide area because of their repulsive interactions or try to overtake one another.” His simulations have found that columns placed asymmetrically in front of door openings can help “reduce the pressure at the door.” As with rice, when you organize the flow, slower is faster. See Dirk Helbing, “Traffic and Related Self-Driven Many-Particle Systems,”
Reviews of Modern Physics,
vol. 73, no. 4 (2001), pp. 1067–1141.

with ramp meters than without: See David Levinson and Lei Zhang, “Ramp Meters on Trial: Evidence from the Twin Cities Metering Holiday,” Department of Civil Engineering, University of Minnesota, May 30, 2002; see also Cambridge Systematics, “Twin Cities Ramp Meter Evaluation,” prepared for Minnesota Department of Transportation, February 1, 2001.

rarely have to stop: Jerry Champa, “Roundabout Intersections: How Slower Can Be Faster,”
California Department of Transportation Journal,
vol. 2 (May–June 2002), pp. 42–47.

1,320 vehicles per hour: Robert Herman and Keith Gardels, “Vehicular Traffic Flow,”
Scientific American,
vol. 209, no. 8 (December 1963).

more lost time: According to one study, SUVs reduce traffic flow in another way as well, by blocking the view of following drivers, who tend to leave more headway as their sight distance drops and they are less sure of traffic conditions ahead. This, of course, diverges from the findings of another study, cited above in the note for the phrase “when they followed passenger cars.” The difference in results may be due to the different types of roads on which the two studies were conducted or some other unidentified artifact. Kara M. Kockelman and Raheel A. Shabih, “Effect of Vehicle Type on the Capacity of Signalized Intersections: The Case of Light-Duty Trucks,”
Journal of Transportation Engineering,
vol. 126, no. 6 (1999), pp. 506–12.

stop on red: See, for example, Matt Helms, “Wait Just Two Seconds Before You Start,”
Free Press,
June 18, 2007.

Drivers talking on cell phones: University of Utah psychology professor David Strayer found in one driving-simulator experiment that subjects talking on a cell phone tended to drive more slowly and make fewer lane changes to avoid slower moving traffic (which may be read as a surrogate for a delayed ability to react). The total of this activity, Strayer estimates, adds 5 to 10 percent to total commuting times (then again, driving more slowly has safety and environmental benefits). See Joel M. Cooper, Ivana Vladisavljevic, David L. Strayer, and Peter T. Martin, “Drivers’ Lane-Changing Behavior While Conversing on Cell Phone in Variable-Density Simulated Highway Environment,” paper submitted to 87th Transportation Research Board meeting, Washington, D.C., 2008.

about 12 miles per hour: Robert L. Bertini and Monica T. Leal, “Empirical Study of Traffic Features at a Freeway Lane Drop,”
Journal of Transportation Engineering,
vol. 131, no. 6 (2005), pp. 397–407.

wreak progressive havoc: See Philip Ball, “Slow, Slow, Quick, Quick, Slow,”
Nature,
April 17, 2000. For the original research, see T. Nagatani, “Traffic Jams Induced by Fluctuation of a Leading Car,”
Physical Review E,
vol. 61 (2000), pp. 3534–40.

effects of a shock wave: See P. Breton, A. Hegy, B. De Schutter, and H. Hellendoorn, “Shock Wave Elimination/Reduction by Optimal Coordination of Variable Speed Limits,”
Proceedings of the IEEE Fifth International Conference on Intelligent Transportation
Systems (ITSC ’02), Singapore, pp. 225–30, September 2002.

trip times declined: Highways Agency,
M25 Controlled Motorways: Summary Report,
November, 2004.

slower can be faster: These systems require careful planning, however, to avoid unintended effects. The speed-limit step-down cannot be too sudden, or that itself could cause a shock wave. The ideal system would be coordinated along the length of the highway, to avoid simply sending one well-coordinated group of drivers smack into another jam farther down the road—and inadvertently helping to extend that jam or cause another one. See, for example, P. Breton et al., “Shock Wave Elimination/Reduction by Optimal Coordination of Variable Speed Limits.”

or the opposite: Boris Kerner notes, “The traffic flow instability is related to a finite reaction time of drivers. This reaction time is responsible for the vehicle over-deceleration effect: if the preceding vehicle begins to decelerate unexpectedly, a driver decelerates stronger than is needed to avoid collisions.” From Boris Kerner,
The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory
(Berlin: Springer, 2004), p. 69.

each car behind it will stop: One simulation compared the “oscillations” and “amplifications” found in stop-and-go traffic to those found in queues. “Perturbations” in the queue, or the way people stopped and started, were often observed to grow larger from the front to the back of the queue in simulators using cellular automata. See Bongsoo Son, Tawan Kim, and Yongjae Lee, “A Simulation Model of Congested Traffic in the Waiting Line,”
Computational Science and Its Applications: ICCSA 2005,
vol. 3481 (2005), pp. 863–69.

the harder it is to predict: An interesting parallel has been drawn between the way nonlinear traffic flows behave and the way supply chains work in the world of business. Supply chains suffer from what has been called the “bullwhip effect”—the farther a supplier is from the consumer, the higher the potential for variability (e.g., oversupply or undersupply). For example, when a person orders a beer in a bar, there is direct communication between the patron and the bartender. The order is placed and then filled. But this immediacy becomes increasingly more difficult moving out along the supply chain. If there is a sudden surge in demand for a type of beer at a bar, the bartender will be instantly aware of this; it will take longer for the brewer of the beer to realize this, and even longer for the grower of the hops (and by the time they react to the changed demand, it may have changed again). In traffic, Carlos Daganzo has pointed out, cars flow through a bottleneck rather smoothly; cars far upstream of the bottleneck, however, experience wide “oscillations” in speed. They are less aware of the actual conditions of supply and demand than those cars moving through the bottleneck. See “The Beer Game and the Bullwhip,”
ITS Berkeley Online Magazine,
vol. 1, no. 2 (Winter 2005).

by the car following them:
Gary A. David and Tait Swenson, “Identification and Simulation of a Common Freeway Accident Mechanism: Collective Responsibility in Freeway Rear-End Collisions,” CTS 06-02. Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, April 2006.

car was given ACC: The ACC study results are described in L. C. Davis, “Effect of Adaptive Cruise Control Systems on Traffic Flow,”
Physical Review E,
vol. 69 (2004).

Chapter Five: Why Women Cause More Congestion Than Men

1.1 hours: Andreas Schafer and David Victor, “The Past and Future of Global Mobility,”
Scientific American,
October 1997, pp. 58–63.

made more frequent, shorter trips: Vacov Zahavi, “The ‘UMOT’ Project,” August 1979, prepared for the U.S. Department of Transportation and the Ministry of Transport, Federal Republic of Germany, Bonn.

in one hour: Cesare Marchetti, “Anthropological Invariants in Travel Behavior,”
Technological Forecasting and Social Change,
vol. 47 (1994), pp. 75–88.

thirty minutes each way: M. Wachs, B. D. Taylor, N. Levine, and P. Ong, “The Changing Commute: A Case-study of the Jobs-Housing Relationship over Time,”
Urban Studies,
vol. 30, no. 10 (1993), pp. 1711–29.

jobs were located: See David Levinson and Ajay Kumar, “The Rational Locator,”
Journal of the American Planning Association,
vol. 60, no. 3 (1994), pp. 319–43. Similar trends have been observed in the Portland area, as described in Robert L. Bertini, “You Are the Traffic Jam: An Examination of Congestion Measures,” paper submitted to Eighty-fifth Annual Meeting of the Transportation Research Board, January 2006, Washington, D.C.

jacking up the numbers: D. Levinson and Y. Wu, “The Rational Locator Reexamined,”
Transportation,
vol. 32 (2005), pp. 187–202.

prompts more driving: See Nancy McGuckin, Susan Liss, and Bryant Gross, “Do More Vehicles Make More Miles?”
National Household Travel Survey
(Washington, D.C.: Federal Highway Administration, 2001).

the worse the traffic congestion: Anthony Downs, “Why Traffic Congestion Is Here to Stay…and Will Get Worse,”
Access Magazine,
no. 25 (Fall 2004). See also Scott F. Festin,
Summary of National and Regional Travel Trends: 1970–1995
(Washington, D.C.: U.S. Department of Transportation, Federal Highway Administration, 1996).

figure is 48 percent: Figures supplied by Alan Pisarski.

roughly 16 percent: Alan Pisarski,
Commuting in America III
(Washington, D.C.: Transportation Research Board, 2006), p. 2.

over 32 miles: Susan Handy, Andrew DeGarmo, and Kelly Clifton,
Understanding the Growth in Non-Work VMT,
Research Report SWUTC/02/167222 (Austin, Texas: Southwest Region University Transportation Center, University of Texas, February 2002), p. 6.

whole day to complete: For a good discussion of recent changes in women’s travel behavior, see Rachel Gossen and Charles Purvis, “Activities, Time, and Travel: Changes in Women’s Travel Time Expenditures, 1990–2000,”
Research on Women’s Issues in Transportation, Report of a Conference, Vol. 2
(Washington, D.C.: Transportation Research Board, 2004).

are now fam-pools: Nancy McGuckin and Nandu Srinivasan, “The Journey-to-Work in the Context of Daily Travel,” paper presented at the Transportation Research Board meeting, Washington, D.C., 2005.

statistically driving more miles: Survey data in the United States indicates what seems like an intuitive fact: The more members in a household, the more miles it drives. “Travel within households increases by household size and income,” as Nancy McGuckin put it to me in an e-mail correspondence.

precocious car poolers: See, for example, Christina Sidecius, “Car Pool Lane Not for Dummies,”
Seattle Times,
August 2, 2007.

more often than men do: See
Research on Women’s Issues in Transportation: Report of a Conference
(Washington, D.C.: Transportation Research Board, National Research Council, 2005), p. 30.

about 15 percent do: Jane Brody, “Turning the Ride to School into a Walk,”
New York Times,
September 11, 2007.

by some 30 percent: See U.S. Environmental Protection Agency,
Travel and Environmental Implications of School Siting,
EPA 231-R-03-004, October 2003, and Department of Environment, Transport and the Regions, London, Greater Vancouver Regional District,
Morning Peak Trip by Purpose,
1999.

sports in America
doubled:
Charles Fishman, “The Smorgasbord Generation,”
American Demographics,
May 1999.

trips are getting longer: Handy, DeGarmo, and Clifton,
Understanding the Growth in Non-Work VMT.

typical rush hours: See
Highway Statistics 2005
(Washington, D.C.: Office of Highway Policy Information, Federal Highway Administration).

closest to their home: Susan L. Handy and Kelly J. Clifton, “Local Shopping as a Strategy for Reducing Automobile Dependence,”
Transportation,
vol. 28, no. 4 (2001), pp. 317–46.

did a few decades ago: Handy, DeGarmo, and Clifton, p. 31.

it was .79 miles: Handy, DeGarmo, and Clifton, p. 29.

was completely alien: See the report by the Technical Committee of the Colorado-Wyoming Section of the Institute for Transportation Engineers, “Trip Generation of Coffee Shops with Combination Drive-Through and Sit-Down Facilities” retrieved from
http://www.cowyite.org/technical/
.

left turn during rush hour: Starbucks also anticipates traffic flow in another way: It likes to locate stores near dry cleaners and video rental shops in order to capture the “dropping off” and “picking up” traffic flows (two chances to sell that double latte). See Taylor Clark,
Starbucked
(New York: Little, Brown, 2007).

stalled queues of cars: Andrew Downie, “Postcard: Brazil,”
Time,
September 27, 2007. The author drily notes: “Motorbikes account for 9% of the city’s vehicles but they cause more accidents than all the rest combined, according to city traffic officials. That means moto-medics also come with a dose of irony.”

all other travel methods: Pisarski,
Commuting in America III,
p. 109.

those without one: “Poverty and Mobility in America,”
NPTS Brief
(Washington, D.C.: U.S. Department of Transportation, Federal Highway Administration, December 2005).

than public transit: See Brian D. Taylor, “Putting a Price on Mobility: Cars and Contradictions in Planning,”
Journal of the American Planning Association,
vol. 72, no. 3 (Summer 2006), pp. 279–84.

near the top: Daniel Kahneman, Alan Krueger, Norbert Schwarz, and Arthur Stone, “A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method,”
Science,
vol. 306, no. 5702 (December 2004), pp. 1776–78.

but sixteen minutes: Mokhtarian raises the point that people in such surveys may be confusing the idea of “ideal commute” with what commute they would be
willing
to make; she also notes that they might be giving what they consider to be a “realistic” ideal and not, say zero minutes. See Patricia L. Mokhtariand and Lothlorien S. Redmond, “The Positive Utility of the Commute: Modeling Ideal Commute Time and Relative Desired Commute Amount,” Berkeley: University of California Transportation Center, Reprint UCTC No. 526.

figuring out alternatives: S. Handy, L. Weston, and Patricia L. Mokhtarian, “Driving by Choice or Necessity?”
Transportation Research Part A: Policy and Practice,
vol. 39, nos. 2–3 (2005), pp. 183–203.

rational perspective: Alois Stutzer and Bruno S. Frey, “Stress That Doesn’t Pay Off: The Commuting Paradox” (September 2004), IZA Discussion Paper No. 1278, Zurich IEER Working Paper No. 151. Available at SSRN:
http://ssrn.com/abstract=408220
.

grown the most: Robert H. Frank,
Falling Behind
(Berkeley: Univ. of California Press, 2007), p. 82.

“hedonic adaptation”: See S. Frederick and G. Loewenstein, “Hedonic Adaptation,” in
Scientific Perspectives on Enjoyment, Suffering, and Well-Being,
ed. D. Kahneman, E. Diener, and N. Schwartz (New York: Russell Sage Foundation, 1999), pp. 303–29.

more prone it is to variability: Nancy McGuckin and Nandu Srinivasan, “The Journey-to-Work in the Context of Daily Travel,” paper presented at the Transportation Research Board meeting, 2005. Washington, D.C.

actual time itself: See, for example, Harry Cohen and Frank Southworth, “On the Measurement and Valulation of Travel Time Variability Due to Incidents on Freeways,”
Journal of Transportation and Statistics,
vol. 2, no. 2 (Dec. 1999), as well as David Brownstone and Kenneth A. Small, “Valuing Time and Reliability: Assessing the Evidence from Road Pricing Demonstrations,”
Transportation Research Part A: Policy and Practice,
vol. 39, no. 4 (2005), pp. 279–93.

“hell every day”: Jonathan Clements, “Money and Happiness? Here’s Why You Won’t Laugh,”
Wall Street Journal,
August 16, 2006.

higher rate than are passenger cars: T. Cohn, “On the Back of the Bus,”
Access,
vol. 21 (1999), pp. 17–21.

into early retirement: The information on urban bus drivers comes primarily from the work of Gary Evans, a professor of human ecology at Cornell University. See, for example, Gary Evans, “Working on the Hot Seat: Urban Bus Drivers,”
Accident Analysis & Prevention,
vol. 26 (1994), pp. 181–93; G. Evans, M. Palsane, and S. Carrere, “Type A Behavior and Occupational Stress: A Cross-cultural Study of Blue-Collar Workers,”
Journal of Personality and Social Psychology,
vol. 52 (1987), pp. 1002–07; and Gary W. Evans and S. Carrere, “Traffic Congestion, Perceived Control, and Psychophysiological Stress Among Urban Bus Drivers,”
Journal of Applied Psychology,
vol. 76 (1991), pp. 658–63.

how much they’re dating: F. Strack, L. L. Martin, and N. Schwarz, “Priming and Communication: The Social Determinants of Information Use in Judgments of Life-Satisfaction,”
European Journal of Social Psychology,
vol. 18, 1988, pp. 429–42.

“focusing illusion”: Daniel Kahneman, Alan B. Krueger, David Schkade, Norbert Schwarz, and Arthur A. Stone, “Would You Be Happier If You Were Richer? A Focusing Illusion,”
Science,
vol. 312, no. 5782 (June 30, 2006), pp. 1908–10.

makes them think it is: We are also quite capable of changing the way we feel about something—or the way we
think
we feel about something—simply by subtly changing our definitions of what is important. A fascinating example of this was seen when a group of psychologists from various countries decided to interview solo drivers before and after a car-pool lane was built on a highway in the Netherlands. They conducted similar interviews on a “control” highway that was not getting a new car-pool lane. When the car-pool lane was added, saving about twenty minutes for those in it, solo drivers’ attitudes seemed to change. It was not as if they suddenly had a more positive opinion of driving alone and a more negative opinion of carpooling, per se. What did change was how important they felt certain aspects of their commute were. Suddenly, “flexibility” ranked as more important, and saving money or travel time less so. On the highway without a car-pool lane, drivers’ attitudes remained the same. But on the highway where the new car-pool lane appeared, teasing solo drivers with its uncongested pleasures, they suddenly had less of a preference for carpooling than when it had not been there. Rather than change their behavior or be haunted every day by not “doing the right thing,” they were suddenly telling themselves new stories about what was important to them. (Interestingly, they did not change their attitudes toward what was best for the environment, even if their own behavior did not follow suit.) They were justifying their actions to themselves—that is, making themselves feel better. It could be that rounding up the car pool would take longer than the lane would save (even if a car pool would still be better for the environment and traffic congestion). It could also be that many people, as mentioned above, simply cannot carpool. But it also seems that people, when actually shown an alternative that would be better for society at large, are good at finding ways to explain why it would not be good for them. A driver stuck in traffic watching a commuter train speed by does not necessarily think, “I wish I were on that train,” but instead tries to console himself with the reasons he cannot be on that train. And so the roads are filled with people wondering why there are so many other people on the roads, all of them convinced of the reasons they need to be there. See Mark Van Vugt, Paul A. M. Van Lange, Ree Meertens, and Jeffrey Joireman, “How a Structural Solution to a Real-World Social Dilemma Failed: A Field Experiment on the First Carpool Lane in Europe,”
Social Psychology Quarterly,
vol. 59 (1996), pp. 364–74.

less than 15 percent: Brian Taylor, “Rethinking Traffic Congestion,”
Access,
Fall 2002, pp. 8-16.

like a bell: There are interesting regional variations on this. In Arizona, for example, it has been observed that parking spaces
closest
to the store are often empty, as cars gravitate first toward the perimeter of the lot, where trees might provide some shade. As one article put it, “A long walk to the store is far better than driving home in a car that has baked for hours in the desert heat.” From Diane Boudreau, “Urban Ecology: A Shady Situation,”
Chain Reaction,
vol. 4 (2003), pp. 18–19. For more on the microclimate differences between tree-shaded parking lots and those without, see Klaus I. Scott, James R. Simpson, and E. Gregory McPherson, “Effects of Tree Cover on Parking Lot Microclimate and Vehicle Emissions,”
Journal of Arboriculture,
vol. 25, no. 3 (May 1999), pp. 129–41.

bell-curve arrangement: This idea was first suggested, as far as I can discern, at the following Web site:
http://vandersluys.ca/?p=7914
.

not necessarily being chosen: Velkey’s findings matched those predicated by two engineering professors in a “probabilistic model.” See C. Richard Cassady and John E. Kobza, “A Probabilistic Approach to Evaluate Strategies for Selecting a Parking Space,”
Transportation Science,
vol. 32, no. 1 (January 1998), pp. 30–42.

to walk somewhere:
Travel Behaviour Research Baseline Survey 2004: Sustainable Travel Demonstration Towns
(SUSTRANS and Socialdata, 2004). Retrieved from
http://www.sustrans.org.uk/webfiles/travelsmart/STDT%20Research%20FINAL.pdf
.