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Record W7072206235

We Can’t Get There from Here: Why Pricing Congestion is Critical to Beating It

2015· article· en· W7072206235 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotorefractive and Nonlinear Optics
Canadian institutionsnot available
Fundersnot available
KeywordsTraffic congestionCongestion pricingSingapore Area Licensing SchemeWork (physics)UrbanizationExternalityRoad pricingTransport economics
DOInot available

Abstract

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Traffic congestion is a growing problem in many of our cities, imposing significant costs on CanadiansCongestion on our roads and freeways leads to wasted time for commuters and goods movement.Given the importance of the movement of goods and people through our cities, this lost time translates into a less efficient economy.The Toronto Board of Trade (2013), for example, estimates that the direct annual costs of congestion for the Greater Toronto and Hamilton Area could rise to $15 billion by 2031 without further action.In some Canadian cities, it takes more than an hour to get to and from work every day for half or more of the residents.Congestion also affects choices about where to live, undercutting the ability of cities to attract businesses, jobs, and workers.And congestion increases air pollution from vehicles, with corresponding health implications for Canadians.This air pollution is related to higher risks of asthma, high blood pressure, cardiovascular disease, diabetes, aggravation, and stress.As cities continue to grow, with higher levels of urbanization and car ownership, traffic congestion and its associated costs are expected to worsen.The higher these costs climb, the greater the benefits from reducing congestion. Congestion pricing is an essential-but missing-piece of smart transportation policyCongestion pricing is an ecofiscal policy that prices road use or parking with the aim of reducing costly traffic congestion.A growing body of evidence and policy experience suggests that congestion pricing works, particularly as part of a broader policy package.When designed well, it leads to reduced traffic congestion and creates net economic benefits both for the economy as a whole and for individual drivers.The case studies examined in this report highlight this point: pricing policies of different kinds have reduced congestion.In Ontario, traffic on the tolled Highway 407 consistently moves at freeflow speeds, while peak travel times on parallel unpriced routes are 50% to 200% longer.Under Stockholm's congestion pricing policy, vehicles entering the city core dropped by 20% to 30%.Minnesota's high-occupancy toll (HOT) lanes increased traffic speeds by 6% in the general-purpose lanes while maintaining free-flow speeds in the toll lanes.In Oregon's pilot project, drivers subjected to higher per-mile charges during peak times responded by reducing driving at those times by 22%, relative to those paying a flat rate.And San Francisco's parking-pricing program led to a 50% decline in the number of drivers circling for a parking spot-a major contributor to downtown traffic congestion.Despite the evidence of its potential benefits, Canada has very limited experience with congestion pricing.The traditional approach to dealing with traffic congestion has been to expand public transit and build more roads.These policies are key components of the transportation puzzle: they increase the overall capacity of the transportation system and can reduce congestion in the short term.In the absence of congestion pricing, more drivers will ultimately fill this increased road capacity, and congestion may not be reduced in the long term.Moreover, the building of new road infrastructure to meet growing demand is constrained by land-use policy and increasingly stretched government budgets. V Executive Summary continuedCongestion pricing is therefore the crucial, missing piece of a broader, coordinated package of policies to create greater mobility for a growing urban population.More public transit, roads, and cycling infrastructure provide drivers with alternatives, making it easier for them to respond to the congestion price by changing their behaviour.They are essential complements to congestion pricing.But without addressing the fundamental issue of misaligned incentives around free access to roads, traffic congestion in Canadian cities will only get worse. The design details of congestion pricing policy matterCongestion pricing is not a one-size-fits-all policy solution.Different cities face different types of congestion problems, and tailoring policies to local circumstances is critical for success.Policy design includes a range of choices.Should pricing be narrowly targeted or broadly applied?That is, should it price access to some roads, to all roads, to parts of roads, or even to parking?How should the price vary?Should it be higher at times of peak traffic, or even vary dynamically in response to real-time traffic levels?How should revenue from the policy be used?Smart policy design can reduce congestion, improving efficient transportation and travel outcomes for all travellers.It can also ensure that low-income travellers are not disproportionately affected.But the specific details of effective, costeffective, fair, and practical policy solution will vary from city to city.How can we move ahead with practical and cost-effective policy to reduce traffic congestion while considering the unique and complex characteristics of each city?This report makes four recommendations for Canadian policymakers. RECOMMENDATION #1: Major Canadian cities should implement congestion pricing pilot projects, customized to their local contextAs illustrated by case studies from Stockholm, Oregon, and San Francisco, trial periods for congestion pricing are low-risk policy initiatives.They can be voluntary for drivers, as in Oregon; take place for a limited time, as in Stockholm; and apply to a narrow scope of drivers, as in San Francisco.Yet the benefits of such trials could be huge.If well designed, they can demonstrate the concrete benefits that congestion pricing can deliver.They can also provide opportunities for learning about how well different policy designs work in different contexts, thus allowing policy design to evolve and improve over time.Municipalities best understand their own congestion context, and should play a major role in designing pilot projects.They

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.285
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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