Congestion Costing Reality Check, Part 1: Congestion evaluation best practices

Congestion Costing Reality Check, Part 1: Congestion evaluation best practices

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Congestion Costing Reality Check, Part 1: Congestion evaluation best practices

Todd Litman
Thu, 07/23/2026 – 05:00

This is the first of a three-part series on how best to define, evaluate and solve traffic congestion problems.
Traffic congestion is frustrating and wasteful, but misguided solutions are even worse. Overestimating congestion costs causes transportation agencies to overinvest in urban highway expansions. Transportation agencies spend a fortune on roadway expansions intended to reduce congestion, but these efforts are costly, ineffective and often harmful overall by inducing more traffic and sprawl . Planning decisions often involve trade-offs between traffic speed and other accessibility factors such as walkability and roadway connectivity, and between speed and other goals such as affordability, safety and fairness. Planners need accurate and comprehensive information on congestion impacts.
Let’s begin with a real-world  example . The #61 bus route connecting my city —Victoria, British Columbia — with the town of Sooke about 40 kilometers west on Highway 14 has 43 daily departures with $3 one-way fares. Because service is frequent and affordable, it carries 22% of peak period person-trips on that corridor. In contrast, the #66 bus route connecting Victoria with the town of Duncan, 60 kilometers to the north over the Malahat Highway, has only four daily departures with $10 one-way fares; because service is infrequent and expensive it carries an insignificant portion of travel on that corridor. Our Ministry of Transportation is considering spending billions of dollars to expand the highway but overlooks simple, low-cost solutions such as frequent and affordable bus service with TDM ridership incentives. This is inefficient — it ignores one of the quickest, most cost-effective and beneficial roadway improvement strategies — and is unfair to the many travellers who cannot, should not or prefer not to drive.
Why do transportation agencies underinvest in efficient and equitable solutions? Because standard transport planning practices exaggerate congestion costs and urban highway expansion benefits and undervalue multimodal planning, TDM incentives and Smart Growth policies that reduce traffic problems by reducing total vehicle travel. They stack the deck in favor of roadway expansions to the detriment of other transportation improvement strategies.
The devil is in the details. Many congestion costing biases are obscure, even to practitioners. They reflect assumptions about the nature of congestion and technical details about how impacts are measured and solutions evaluated.
I’ve become obsessed with these issues. My new report,  Congestion Costing Best Practices , describes how best to evaluate traffic problems and solutions. Its companion report,  Congestion Costing Critique , finds that commonly cited congestion costing studies often violate best practices, resulting in exaggerated cost estimates.  Smart Congestion Relief provides guidance for choosing truly optimal congestion reduction strategies considering all impacts and goals. Over the next three weeks we’ll explore this research and its implications for planners.
This is an important and timely issue. Per capita vehicle travel grew steadily during the 20th century. During that period, it made sense to invest significant resources to expand roads to accommodate growing traffic. In 1950, a transportation planner could rationally overbuild a highway in anticipation of future needs. However, early in the 21st century, per capita vehicle travel  peaked , we became increasingly aware of the problems that result from  overbuilding urban highways, and there is growing demand for non-auto travel. Although few motorists want to forego driving altogether, surveys indicate that many would prefer to  drive less and rely more on alternatives , provided those alternatives are convenient, comfortable and affordable. Multimodal planning responds to those changing demands.
In addition, we have a better understanding of congestion dynamics. In the past, traffic was often modelled as a fluid that flows through a road system, but experts now recognize that it often behaves more like a gas that fills available space and can be condensed with appropriate incentives. Congestion tends to self-limit: it increases until delays cause some travellers to avoid potential peak-period vehicle trips, resulting in equilibrium, as illustrated below.
Traffic growth and congestion equilibrium ( Litman 2025 )
Traffic grows until delays discourage more traffic growth, creating a self-limiting equilibrium (indicated by the curve becoming horizontal). Extrapolating past trends without considering these effects exaggerates future congestion problems ("Gridlock"). Expanding road capacity causes traffic to grow until congestion returns, resulting in a new equilibrium with larger traffic volumes. This equilibrium depends on the quality of alternatives, such as public transit comfort, speed and affordability, and TDM incentives such as road and parking pricing, flextime and telework.
Congestion equilibrium levels depend on the quality of options and incentives: if alternatives are inconvenient and expensive, congestion will become severe before a sufficient number of urban-peak vehicle trips shift, but if urban travellers have high quality public transit, convenient telework and services that are easy to reach by walking and bicycle, travellers can more easily reduce peak period driving and congestion costs.
This has important implications for planning. First, congestion problems seldom become as severe as predicted by extrapolating past trends; urban roads often experience moderate congestion (LOS C or D) but seldom maintain severe congestion (LOS E or F). Second, urban road expansions usually provide only temporary congestion reductions because the added capacity fills with latent traffic, which increases total vehicle travel and future traffic problems. For example, expanding an urban freeway increases surface-street traffic volumes and congestion problems. Third, improving alternative modes helps reduce congestion, in addition to many other benefits.
Congestion evaluation best practices
It is time to rethink how we evaluate congestion costs and potential solutions. Considering the importance that transportation agencies give to congestion costs and the huge amounts they spend to alleviate it, you might expect there to be abundant guidance on the subject, but surprisingly, it does not exist. Most available literature is  old ,  foreign or too  complex and technical for most users. I first tried to fill this gap with a short paper, Congestion Costing Best Practices, presented at a  2014 TRB Conference , which I’ve updated over time to be more comprehensive and current. The new, revised version provides practical guidance for congestion impact analysis. The table below describes best practices for ten key analysis factors.
Congestion evaluation best practices summary
Evaluation Factor
Recommended Best Practices
Congestion metrics – how congestion is measured.
For planning, pricing and equity analysis, measure internal and external impacts on all modes based on deadweight losses.
Baseline speeds – Traffic speeds considered optimal
Use economically optimal baseline speeds that maximize efficiency and reflect users’ willingness to pay for faster travel.
Traffic data accuracy – how sampled speed data are applied to total vehicle travel.
Recognize traffic app data biases. Adjust data to accurately reflect congestion delays experienced by average motorists.
Congestion exposure – the amount of travel that experiences congestion.
Use realistic estimates of congested vehicle travel. Measure congestion costs per capita or commuter, not per motorist or auto commuter.
Peak to off-peak speed trends – how speed differentials are calculated and interpreted.
Recognize that faster off-peak traffic increases estimated congestion delay hours.
Travel time valuation – monetary costs assigned to delay.
Use realistic values of time that reflect travellers’ willingness-to-pay, such as 30-50% of average wages, unless other values are justified.
Additional impacts – impacts on fuel, risk, emissions, walkability and productivity.
Use best current models to analyze impacts of changes in speed, delay and vehicle travel. Strive for optimal speeds, mode shares and prices.
Congestion dynamics – the tendency of congestion to affect traffic and travel.
Recognize that congestion can be self-limiting, the quality of alternatives affects equilibrium levels, and effects of induced vehicle travel.
Equity analysis – the distribution of impacts and whether that is considered fair.
Determine how costs and benefits affect different groups, particularly people with disabilities, low incomes or other special needs.
Research standards – whether results are credible, understandable and replicable.
Maintain professional standards including transparent methods and data to allow replication, comprehensive references, plus peer review.
This table summarizes factors to consider in congestion costing, and their best practices.
Whose perspective does a study reflect?
What perspective and scope should be used for congestion cost analysis? Many studies only reflect a motorist’s perspective. They define congestion as a cost that motorists bear, due to inadequate road capacity, which implies that drivers are victims of unresponsive planning and deserve roadway expansions. However, congestion can also be defined as a cost that motorists impose on other road users due to the much larger travel space they require compared with other modes, as illustrated below, which recognizes that motorists are responsible for the problem and should bear the costs. Most studies only measure the internal costs motorists bear, but many policy decisions such as strategic planning, efficient pricing and equity analysis should be based on the marginal external costs they impose on others.
Congestion costs imposed by mode
Road space requirements, and therefore congestion costs imposed, increase with vehicle size and speed, and decline with vehicle occupancy rates. Car travel imposes much more per passenger congestion costs than other modes.
Most studies only report impacts on motorists, such as congestion delays and dollar costs per car commuter, which ignores the congestion avoided when travellers shift to other modes, and the costs that congestion reduction strategies impose on other road users. For example, analyses that only consider impacts on motorists indicate that congestion increases with development density , but multimodal analysis that considers impacts on all travellers indicates the opposite because residents of compact, multimodal neighborhoods drive less under urban-peak conditions. Similarly, multimodal analysis recognizes that wider roads with higher traffic speeds degrade walkability, called the  barrier effect , reducing non-automotive accessibility.
This  research indicates that conventional planning overemphasizes driving compared with other modes, speed compared with other accessibility factors, and congestion compared with other transportation problems. Traffic speed and delay have only modest effects on urban travellers’ ability to access services and activities; equally important are the quality of non-auto modes, transport network connectivity, proximity, affordability and travel information. As a result, urban roadway expansions can degrade transportation system efficiency by reducing other accessibility factors such as walkability, connectivity or development density.
Studies such as the Urban Mobility Report, the INRIX Global Traffic Scorecard and the TomTom Traffic Index claim that congestion costs are large and increasing, but  my research shows that much of their estimated congestion costs consist of traffic law compliance — drivers reducing speeds to what is legal, efficient and safe — and much of their estimated long-term traffic delay growth reflects faster off-peak traffic rather than increased congestion delay. I estimate that these studies exaggerate congestion costs by an order of magnitude.
New technologies can reduce congestion costs in ways that current analysis often overlooks. Traffic apps such as INRIX, TomTom and Wayz help travellers optimize urban travel. Improving travel convenience and comfort can reduce travel time unit costs. Mobility substitutes such as  telework and delivery services reduce the need to travel. These strategies increase transportation system efficiency even if they do not reduce congestion delay hours.
Misvaluing congestion is harmful. Planning decisions often involve trade-offs between congestion reduction and other goals. Exaggerating congestion costs and roadway expansion benefits causes planning to overinvest in automobile facilities and underinvest in other modes and strategies. More accurate analysis that puts traffic speed and delay into perspective allows the planning process to give appropriate attention to other goals such as affordability, safety and fairness.
Accuracy in practice
To illustrate these effects, consider this example. Residents of a compact, multimodal neighborhood use non-auto modes for most trips, but experience relatively intense congestion due to through traffic. Residents of a nearby suburb drive ten times as much but experience modest congestion due to minimal through traffic. Since most congestion cost studies measure delays per motorist, they rate the central neighborhood as more congested than the suburb, despite low congestion costs per capita. Wider roads with more vehicle traffic cause pedestrian delay, but most congestion studies ignore this impact. Since the studies only report the congestion costs that motorists bear, they do not reflect the congestion costs that suburban motorists impose on other modes and communities. Consider how these biases affect planning decisions:
Analyses that only consider delays to motorists favor sprawl, while comprehensive analysis favors Smart Growth policies that encourage compact, multimodal development.
Analyses that ignore induced vehicle travel overinvest in highway expansions, while comprehensive analysis supports more multimodal planning, including bus lanes, and TDM incentives that discourage vehicle traffic growth.
Analyses that frame congestion as a cost that motorists bear imply that motorists deserve urban roadway expansion even if they require subsidies paid by non-drivers, but framing congestion as a cost that motorists impose justifies TDM incentives that discourage urban-peak driving and efficient road tolls that charge drivers for road usage.
Due to these biases, conventional analysis results in more  sprawl and  automobile dependency and less  multimodal planning ,  TDM   incentives and more efficient and  equitable   Smart Growth policies. This is unfair to travellers who  cannot, should not or prefer not to drive and harms motorists by increasing their congestion delays, crash risk and  chauffeuring burdens compared with what would result from best evaluation practices.
Current congestion costing reflects a simplistic approach which only quantifies easy-to-measure impacts such as traffic speed and vehicle delay. For efficiency and equity, we need a more sophisticated analysis that considers all travellers, not just motorists; accounts for all accessibility factors including non-auto travel, connectivity and proximity; reflects qualitative factors such as travel comfort and convenience; and accounts for the value of travel and the benefits of prioritizing higher-value trips. Best practices identify how to do so.
I hope this has piqued your interest in congestion cost analysis! Over the next two parts of the series we will investigate biases in congestion cost studies and identify truly effective and beneficial congestion reduction strategies.
Please let me know what you think of this research below in the comments section or by  email .

Category
Transportation

Tags
Congestion Costing Reality Check
Traffic
Congestion
Auto-Centric Planning
Mobility
Multimodal Transportation

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