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Record W2884605257 · doi:10.5604/01.3001.0012.2102

Estimating saturation flow under weak discipline traffic conditions, case study: Iran

2018· article· en· W2884605257 on OpenAlexaboutno aff
Maryam Dehghani-Zadeh, Mehdi Fallah Tafti

Bibliographic record

VenueArchives of Transport · 2018
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsBottleneckSaturation (graph theory)Transport engineeringComputer scienceIndonesianData collectionTraffic flow (computer networking)Degree of saturationOperations researchEngineeringEnvironmental scienceStatisticsMathematicsComputer security

Abstract

fetched live from OpenAlex

Intersections, as the critical elements and the major bottleneck points of urban street networks, may have inconsistent performances in different countries. This is largely due to the fact that the factors affecting their performance e.g. driving behavior, vehicle characteristics, control methods, and environmental conditions may vary from one country to another. It is, therefore required to take into account these factors when developing or applying available models and methodologies for their capacity analysis or signal control setting. This is particularly important for the countries with heterogeneous and weak discipline traffic streams such as Iran. Meanwhile, estimating the saturation flow rate, which is a key parameter in capacity and delay analysis and in optimal timing of traffic signals, is of great importance. In this study, the possibility of identifying and or developing appropriate models for estimating the saturation flow rate at the signalized intersections in these situations has been explored. For this purpose, a case study performed at the signalized intersections located in the city of Yazd, a medium sized city located in the middle of Iran. Using the data obtained from several intersections together with the application of analytical procedures proposed by American, Australian, Canadian, Indonesian, Iranian and Malaysian highway capacity guides, the saturation flow rate was estimated from both field observations and analytical methods. A comparison of these results indicated that in the protected left-turn situations, the Australian guide produced the best comparable results with the field data. On the other hand, in the permitted left-turn situations, the method proposed in the American Highway Capacity Manual guide produced the best comparable results with the field data. Furthermore, three new models were developed for estimating the saturation flow rate in three different situations namely, unopposed mixed straight and turning traffic movements, opposed mixed straight and turning traffic movements and merely straight through movement. The effective width, traffic composition, and opposite oncoming through traffic flow were considered as the effective parameters in the proposed models. Moreover, using the multivariate regression analysis, the Passenger Car Equivalent coefficients for motorcycles and heavy vehicles were calculated as 0.51 and 2.09, respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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