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Record W2737994427

Regulating On-Street Parking: Evidence from Danish data

2012· article· en· W2737994427 on OpenAlexaff
Edith Madsen, Ismir Mulalic, Ninette Pilegaard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsTransport Canada
Fundersnot available
KeywordsDanishGeographyTransport engineeringComputer scienceCartographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Around the world, cities use a range of types of policy to organize the parking market, e.g. physical planning, parking fees, restrictions on the maximum duration for on-street parking, introduction of parking permits, etc. This paper deals with the parking pricing. A small but rapidly growing scientific literature analyzes parking. The economic literature has gathered around the idea that parking should be priced at its opportunity cost, just like any other commodity. Small and Verhoef (2007) point to the fact that parking is underpriced in many urban areas. The main consequence of underpricing is cruising for parking and cruising for parking is a pure loss from the perspective of the society (Shoup, 2005; Calthrop and Proost, 2006). This problem is actually so large that Arnott and Inci (2006) finds that cruising for parking should optimally be eliminated. This is in the first best situation done by setting the parking fee large enough to eliminate cruising for parking without having excess supply of parking spaces, and in the second best situation where parking fees cannot be set optimally by increasing the number of parking spaces to eliminate the cruising for parking; again without having excess supply of parking spaces. Moreover, Arnott et al. (1991) finds that a spatially di¤erentiated parking fee is necessary to induce the optimal parking pattern. Finally, Arnott et al. (2005) identify a potential triple dividend from pricing parking (reduce the search for parking, reduce congestion and the use of parking revenues to lower other taxes). So, parking pricing can be used as a part of package of transport regulating measures to internalize congestion and local environmental issues. On the other hand, hourly parking fees may to some extent result in shorter parking durations and thus increase traffic congestion by increasing parking turnover (Arnott and Inci, 2006). Glazer and Niskanen (1992) show that the impact of parking fees on congestion depends largely on the characteristics of the drivers in the relevant region. So, parking pricing may also increase traffic congestion. Therefore it is difficult in practice to find the correct amount of parking fees and parking spaces to eliminate cruising for parking and avoid excess supply of parking spaces. In order to determine optimal set of parking fees and optimal supply of parking spaces it is necessary to find demand elasticity with respect to prices for parking in the area of interest. In this paper the focus is on the demand elasticity for parking. Although the parking issues are treated in the literature the subject still deserves attention and is underresearched and there is hardly any empirical work. One exception is van Ommeren et al. (2012) who estimate the cost of cruising for parking in Amsterdam. This paper adds to the literature by empirically analyzing the demand for on-street parking in Copenhagen based on detailed census data for on-street parking in different parts of the city of Copenhagen for period 2008-2011 where different price levels and other parking restricting methods are used. The panel dataset used includes 6 counts (September 2008, April 2009, September 2009, April 2010, September 2010, and April 2011) of on-street parking in the area of Copenhagen including the number of legal parking spaces and the number of occupied parking spaces for more than 700 streets. In Copenhagen the alternatives of o¤-street parking is limited, so it is reasonable to consider only on-street parking. The focus is on the proportion of parking spaces occupied. This is of interest as it is fundamental to know when and where there is excess demand for parking (which will introduce cruising and other externalities) or excess supply (which is very costly). The paper also deals with the general cost of parking consisting of direct cost (a parking fee) and an indirect cost. The last term reflects the searching costs (cruising) and these are increasing in the occupancy rate. Taking these two effects into account implies that the demand for parking will be more price elastic when the occupancy rate is relatively low (the search cost are low) whereas it will be less price elastic when the occupancy rate is relatively high (increasing the price lowers the demand which in turn reduces the search cost so altogether the total cost will not change much). The empirical results are in line with this. In many streets in Copenhagen the occupancy rate is very high and in fact close to 100% and the estimation results suggest that car drivers in Copenhagen only react slightly to increases in parking fee (DKK/hour).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.178
GPT teacher head0.335
Teacher spread0.157 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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