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Smart Congestion Costing: A Critical Evaluation of the 'Urban Mobility Report'

The new "Urban Mobility Report" provides widely-cited congestion cost estimates. However, its analysis is neither comprehensive nor objective. Anybody using these estimates should understand its omissions and biases.
Todd Litman | August 29, 2019, 8am PDT
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Planners, decision-makers and the general public need comprehensive and objective information on transportation costs, and the likely benefits of potential transportation improvement strategies. The Texas Transportation Institute's "Urban Mobility Report" (UMR) provides widely cited estimates of congestion costs and congestion reduction recommendations. I have criticized previous versions (see "Faulty Assumptions in the TTI Urban Mobility Report" [2011], "Critiquing the Urban Mobility Report" [2013], "Urban Mobility Scorecard: Still Measuring Urban Travel Conditions Incorrectly" [2015]) for using incomplete and biased analysis, and failing to apply academic standards. A new edition was released last week. Unfortunately, it is no better. In fact, it now provides less detailed information about its assumptions and methods.

I will briefly describe some major problems with the UMR's analysis methods. For more information see my detailed report, "Congestion Costing Critique: Critical Evaluation of the 'Urban Mobility Report'" [pdf] and Joe Cortright's, The Top Twenty Reasons to Ignore TTI’s Latest Urban Mobility Report.

Problems with UMR Analysis: 

  • Although it claims to measure urban mobility, it really only considers automobile traffic congestion, ignoring other travel modes and impacts.
  • It reflects mobility rather than overall accessibility, which makes it incomplete and outdated, since urban transport planning increasingly emphasizes accessibility-based analysis.
  • It uses higher baseline speeds and travel time cost values than experts recommend. Much of its estimated congestion costs consist of traffic speeds declining to legal limits. Is it really appropriate to call traffic speed compliance a "cost"?
  • It exaggerates fuel savings and emission reductions. It assumes that any increase in traffic speeds reduces vehicle emissions, although the best available research indicates that emission rates increase above about 50 MPH.
  • It ignores generated traffic impacts, such as increased crashes and pollution from road expansions.

As a result of these omissions and biases the UMR tends to overestimate congestion costs and roadway expansion benefits and undervalues other congestion reduction strategies that provide additional benefits, in addition to reducing congestion. Its methods and results are at odds with most other congestion cost studies. Its $166 billion annual congestion cost estimate is about twice the $87 billion estimated by INRIX, the organization that provides the basic data used in UMR's analysis. The figure below compares the UMR's cost estimate with those resulting from input values recommended by most economists. Its cost estimates should be considered upper-bound values that are significantly higher than results using more realistic assumptions.

Congestion Cost Estimates Compared

The "Urban Mobility Report" uses upper-bound baseline speeds and travel time unit costs. Most economists recommend lower values. The lower-range estimate is based on Transport Canada's lower baseline speed and the U.S. Department of Transportation's lower travel time unit costs, reflecting reasonable lower-bound values published by major organizations.


One of the UMR's themes is that most North Americans must or prefer to drive, and so prefer roadway expansions over policies that shift travel to other modes or create more compact communities. This is why they can use the term commuter when they really mean automobile commuter or urban peak driver. Yet, this is another exaggeration. In fact, automobile mode shares tend to decline as traffic congestion increases. For example, although public transit typically serves just 1-3% of total regional trips, it represents a larger portion of urban commuting (typically 5-10%) and an even greater share (typically 10-50%) of peak-period travel to major activity centers such as central business districts (CBDs) and campuses, as illustrated below.


The UMR also ignores basic research principles. It contains no literature review, fails to clearly explain its assumptions or document sources, does not discuss potential biases, has no sensitivity analysis, and lacks independent peer review. The current edition provides less information about its methods than previous versions. It does not give readers the information they need to understand its results.

The table below evaluates how well the UMR's analysis reflects best practices. 

Congestion Costing Best Practices

Factor

Recommended Best Practices

UMR Practices

Modes considered

Consider impacts on all modes

Generally ignores impacts non-auto modes. Often refers to “commuters” when the analysis only counts automobile commuters.

Baseline speeds

Capacity or economic efficiency optimizing speeds.

Uses freeflow speeds, 30-50% higher than most experts recommend, which often exceed legal speed limits. No discussion of this issue.

Travel time valuation

25-50% of average wages; USDOT recommends $8.37 to $14.34 per hour.

Uses $16.79 per hour based on 1986 Texas study. No discussion of why this was chosen over USDOT recommended values.

Fuel consumption and emission impacts

Recognize that fuel consumption and emissions are lowest at 45-55 mph.

Assumes any traffic speed increase reduces fuel consumption and emission rates.

Safety impacts

Recognize that increasing traffic speeds can increase crash casualty rates.

Ignores this impact.

Future congestion costs

Account for demographic and economic factors that affect future congestion costs.

Extrapolates growth without considering demographic trends or new transport options.

Generated traffic and induced travel impacts

Recognize that roadway expansions often provide little long-term congestion reduction and increase external costs.

Ignores generated traffic and induced travel impacts.

Congestion intensity versus costs

Primarily use per capita congestion costs instead of congestion intensity indicators.

Emphasizes congestion intensity indicators for most comparisons.

In various ways the UMR fails to reflect best current congestion evaluation practices. Its cost estimates should be considered upper-bound values.


How This Affects Planning 

These analysis biases are significant because planning decisions often involve trade-offs between different problems and solutions. For example, road space can either be used for general traffic lanes or bus lanes, and money spent to expand roads is unavailable for other purposes. By exaggerating congestion costs relative to other impacts the UMR tends to overvalue urban roadway expansions and undervalue other transportation improvement strategies that provide other benefits. 

What difference does it make whether U.S. national congestion costs are estimated to total $40 billion, $87 billion, or $166 billion? These numbers are beyond anybody's comprehension. However, congestion costs are often incorporated into planning analyses, such as how to improve urban transportation in a particular situation. By exaggerating congestion compared with other transportation costs, such as consumer costs, accidents and pollution emissions, and by ignoring the incremental external costs of induced travel caused by roadway expansions, the UMR's results overvalue roadway expansions and undervalue other transportation improvement strategies such as improved mobility options, more efficient pricing, policies that create more compact development, and TDM programs that encourage travelers to use more space-efficient modes.

In fact, traffic congestion is a modest cost overall. Congestion cost estimates range from $130 (50% baseline speeds and $9.06 per hour time costs) up to $500 (the UMR's estimate) annual per capita, compared with approximately $3,000 in vehicle ownership costs, $2,000 in crash damages, $1,800 in parking costs, $600 in pollution damage costs, and $400 in roadway costs. 

As a result, a congestion reduction strategy provides far smaller total benefits if it increases other costs, but provides far larger benefits if it helps reduce other costs. For example, a roadway expansion provides less benefits if by inducing additional vehicle travel it increases parking subsidy costs, accidents or pollution costs imposed on other people, while a bicycle or public transit improvement is worth far more if it improves mobility for non-drivers, improves public fitness and health, or reduces total pollution emissions in addition to reducing traffic congestion.

The UMR's analysis tends to favor automobile-dependent sprawl over compact, multi-modal development. For example, it ranks compact, multi-modal cities such as Boston, New York, and Washington, D.C. as having worst congestion than more sprawled, automobile-dependent cities such as Atlanta, Houston, and Miami, but fails to mention that this reflects congestion costs measured per motorist, and if impacts were instead measured per commuter, multi-modal urban regions tend to rate better than sprawled cities, because they have relatively low automobile mode shares. Similarly, if measured based on access to jobs and services, or per capita transportation costs, compact, multi-modal regions tend to rank better than sprawled, automobile-dependent areas.

Impacts of Omissions and Biases on Planning Decisions

Omissions and Biases

Impacts on Planning Decisions

Lacks a current literature review and so fails to identify best current congestion evaluation practices.

Prevents readers from understanding the report’s context and potential biases.

Fails to explain its assumptions.

Prevents readers from understanding the study’s methods or from replicating, critiquing and building on its analysis.

Assumes that transportation means automobile travel. Uses commuter when only automobile travel is measured.

Undervalues non-automotive modes. Skews planning decisions to favor roadway improvements over other types of transport improvements.

Ignores important accessibility factors and impacts, including the quality of non-automobile modes, transport network connectivity and land use proximity.

Favors roadway expansion over other accessibility improvements such as improving alternative modes, network connectivity and land use proximity.

Uses baseline speeds and travel time values higher than most economists recommend.

Exaggerates congestion costs.

Fails to compare congestion with other transport costs. Calls congestion costs “massive,” although they increase travel time and fuel consumption 2% at most.

Exaggerates congestion costs relative to other economic impacts, and therefore congestion reduction compared with other planning objectives

Ignores induced travel impacts.

Exaggerates roadway expansion benefits relative to other transportation improvement strategies.

Uses a constantly declining speed-emission curve.

Exaggerates roadway expansion fuel saving and emission reductions.

Ignores demographic and economic trends which are reducing motor vehicle traffic growth and increasing demand for alternative modes.

Exaggerates future congestion problems and long-term roadway expansion benefits.

Ignores positive trends, including recent declines in congestion, improved technologies and travel options that allow travelers to avoid congestion.

Exaggerates future congestion problems and the benefits of urban roadway expansions.

Lacks independent peer review.

Reduces the study’s ability to identify and correct omissions and biases in analysis.

Ignores criticism.

Reduces the study’s contribution to the profession’s dialogue concerning best congestion costing practices.

The "Urban Mobility Report" contains various omissions and biases which affect planning decisions.

Smarter Planning 

The UMR is propaganda: information selected to promote a particular ideology or policy. It is intended to define traffic congestion as major problem and to justify highway expansions. This is evident in the report press release which describes congestion as "gridlock," and recommends more roads as its first solution. The press release and report use the term commuter when they really mean automobile commuter, and they describe motorists as congestion victims without acknowledging that they also perpetrators. This victimization makes it difficult to implement the most effective congestion reduction strategies, such as decongestion pricing, HOV and bus-lanes, and parking policy reforms, because they are perceived as adding to motorists' burdens. It is notable that the UMR recommends more roads, transit, telework, adjusting work hours and smarter land use, but never mentions pricing reforms or TDM programs.  

In fact, traffic congestion tends to maintain equilibrium: it increases to the point that delays discourage some potential peak-period trips. As a result, it is wrong to extrapolate trends to predict future congestion problems - it almost never reaches true gridlock - and expanding roadway supply seldom provides durable reductions. The most effective way to reduce congestion over the long run is to reduce the point of equilibrium by improving alternative modes, particularly high-quality transit, and applying decongestion pricing (road tolls and parking fees that are higher under congested conditions and decline or disappear at other times and places).

The fact that there is little support by motorists for decongestion pricing or major tax increases to finance roadway expansion is empirical evidence that they do not really consider congestion a major cost; most motorists would rather complain than pay to reduce their congestion delays. 

Traffic congestion is to urban travelers what tides are to sailors, a force that they take into account when planning trips. For example, shippers adjust delivery schedules to avoid rush hour, commuters choose where to live or work, and their commute mode, based on congestion intensity, and residents run errands during off-peak periods. It's a constraint, but smart travelers work with it to minimize their costs. The UMR ignores these issues.

The UMR also does not help its case when it comes to explaining its methods. It lacks contextual information: there is no literature review, it does not explain its assumptions, does not discuss potential biases, it includes no sensitivity analysis, and lacks peer review. There are also few citations and no discussion of why they chose their specific methods and values. As a result, readers are unable to verify the report's results or perform additional research.

The UMR's approach is a throwback to an earlier age. Most planning professionals and jurisdictions are shifting from purely automobile-oriented performance evaluation, using indicators such as roadway level of service and the Travel Time Index, to more multi-modal [pdf] and accessibility-based [pdf] indicators, such as average commute duration, the number of jobs accessible within a 30-minute commute by auto and other modes, and per capita Vehicle Miles Travelled (VMT), assuming that less is better [pdf]. Many jurisdictions now require these indicators and targets; for example, California law and Washington State laws mandate VMT reduction targets, and some cities [pdf] have similar policies. A report which assumes that automobile congestion is the only significant urban transportation problem, without considering other modes, goals and impacts, is increasingly outdated. 

This is not to deny that traffic congestion is a problem, but comprehensive and objective evaluation is required to identify truly optimal solutions, considering all goals and impacts. For more information see "Congestion Costing Critique: Critical Evaluation of the 'Urban Mobility Report'" [pdf] and other publications listed below.

For More Information

Alexander York Bigazzi and Miguel Figliozzi (2012), “Congestion and Emissions Mitigation: A Comparison of Capacity, Demand, and Vehicle Based Strategies,” Transportation Research Part D: Transport and Environment, Vol. 17, pp. 538-547.

Joe Cortright (2019), The Top Twenty Reasons to Ignore TTI’s Latest Urban Mobility Report, City Observatory.

Eric Dumbaugh (2012), Rethinking the Economics of Traffic Congestion, Atlantic Cities.

Susan Grant-Muller and James Laird (2007), International Literature Review of the Costs of Road Traffic Congestion, Scottish Executive.

Daniel Herriges (2019), The Mobility Trap: Why We'll Never Fix Congestion by Speeding Up Traffic, Strong Towns.

INRIX (2019), 2018 Global Traffic Scorecard.

Jonathan Levine, Joe Grengs, Qingyun Shen and Qing Shen (2012), “Does Accessibility Require Density or Speed?Journal of the American Planning Association, Vol. 78, No. 2, pp. 157-172.

Todd Litman (2014), Urban Mobility Report Point-Counter-Point, Victoria Transport Policy Institute.

Todd Litman (2019), Congestion Costing Critique: Critical Evaluation of the “Urban Mobility Report”, Victoria Transport Policy Institute.

Todd Litman (2019), Smart Congestion Relief: Comprehensive Analysis of Traffic Congestion Costs and Congestion Reduction Benefits, initially presented as Transportation Research Board paper P12-5310.

Bruce Schaller (2019), What Urban Sprawl Is Really Doing to Your Commute. Urban Traffic Congestion is Growing Dramatically, According to a New Report. So Why Aren’t Drivers Taking Longer to get to Work?, City Lab.

Eric Sundquist and Bill Holloway (2013), Does The Travel-Time Index Really Reflect Performance?, State Smart Transportation Initiative.

Matthias Sweet (2013), “Traffic Congestion’s Economic Impacts: Evidence from US Metropolitan Regions,” Urban Studies, Vol. 50, No. 15.

TC (2006), The Cost of Urban Congestion in Canada, Transport Canada.

Ian Wallis and David Lupton (2013), The Costs Of Congestion Reappraised, Report 489, New Zealand Transport Agency.

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