MétaCan
Menu
Back to cohort
Record W1761148167 · doi:10.1002/atr.1329

The effects of on‐street parking on the service rate of nearby intersections

2015· article· en· W1761148167 on OpenAlexvenueno aff
Jin Cao, Mónica Menéndez, Vasileios Nikias

Bibliographic record

VenueJournal of Advanced Transportation · 2015
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersEidgenössische Technische Hochschule Zürich
KeywordsIntersection (aeronautics)BottleneckTransport engineeringLevel of serviceTraffic flow (computer networking)Service (business)Relation (database)Computer scienceReduction (mathematics)Parking guidance and informationEngineeringMathematicsComputer networkBusinessData mining

Abstract

fetched live from OpenAlex

Summary An on‐street parking maneuver can often start a temporary bottleneck, leading to additional delay endured by the following vehicles. If the maneuver occurs near a signalized intersection, the service rate of the intersection might be reduced. In this paper, a model is built to analyze the effects of parking maneuvers on the intersection service rate. Based on the hydrodynamic theory of traffic flow, the perturbation caused by the parking maneuver is analyzed. Using dimensional analysis, we illustrate the relation between the background conditions, the distance from the parking area to the intersection, and the intersection service rate. Based on this relation, one can compute the service rate reduction caused by existing on‐street parking areas. A minimum distance between the parking area and the intersection to avoid such reduction can be accordingly found. Numerical examples based on empirical data from the city of Zurich, Switzerland, are provided to illustrate the practical applications. Although the analysis is based on streets with a single lane per direction, the findings can provide some insights regarding different situations. We hope such findings can be used as a basis for developing on‐street parking design guidelines. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.141

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations22
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Advanced TransportationSame topicTraffic control and managementFrench-language works237,207