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Record W1697015183 · doi:10.1002/atr.197

Modeling and optimization of transportation systems

2011· article· en· W1697015183 on OpenAlexaffvenueabout
S. C. Wirasinghe

Bibliographic record

VenueJournal of Advanced Transportation · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOperations researchPublic transportService (business)Transport engineeringComputer scienceSizingEngineeringBusinessMarketing

Abstract

fetched live from OpenAlex

I am pleased to write a short editorial for this special issue, which highlights several areas of strength of the Journal of Advanced Transportation such as public transport and airport systems, transportation planning, techniques of transport systems analysis, and multiobjective optimization of transport systems. The emerging area of evacuation planning is also represented. The issue coincidentally highlights the truly global nature of our authors, as well as the interest that we have in attracting outstanding senior as well as emerging authors. A sister special issue on transport and traffic networks will be edited by Co-Editor-in-Chief William Lam. The papers that have been selected for this special issue are “On the allocation of new lines in a competitive transit network with uncertain demand and scale economies” by Zhi-Chun Li, William H. K. Lam, and S. C. Wong; “Simulation-based analysis of personal rapid transit systems: service and energy performance assessment of the Masdar City PRT Case” by Katharina Mueller and Sgouris P. Sgouridis; “Model of personal attitudes towards transit service quality” by Khandker M. Nurul Habib, Lina Kattan, and Md. Tazul Islam; “IF-EM: an interval-parameter fuzzy linear programming model for environment-oriented evacuation planning under uncertainty” by Qian Tan, Guo H. Huang, Chaozhong Wu, and Yanpeng Cai; “Airport gate reassignments considering deterministic and stochastic flight departure/arrival times” by Shangyao Yan, Ching-Hui Tang, and Yu-Zhou Hou; and “Multiple objective optimization of the fleet sizing problem for road freight transportation” by Jacek Żak, Adam Redmer, and Piotr Sawicki. Li et al. consider an innovative model for allocating new transit lines in a competitive network with demand uncertainty and scale economies. They explicitly consider interactions among the transit authority, transit operators, and transit passengers. Mueller and Sgouridis consider a zero-emission model city that is implementing a personal rapid transit system. The PRT will run throughout the city on an underground network. A discrete-event simulation model that supports the design and implementation of the system is described. Habib et al. present an investigation of the reasons for the use of transit in Calgary, Canada. The reasons are expressed as functions of people's perceptions and attitudes towards transit service quality. A multinomial logit model is developed to capture unobservable latent variables useful in defining passenger perceptions and attitudes. Tan et al. consider uncertainty related to factors involved in evacuation activities. The paper employs inexact optimization techniques to address the uncertainties. An interval-parameter fuzzy model is developed for evacuation management under uncertainty. Yan et al. develop an airport gate reassignment model that considers both deterministic (near future) and stochastic (later) flight departure/arrival times. An integer programming technique is applied to formulate a gate reassignment model (GRM). Because gate reassignment needs to be done repeatedly, the GRM is applied within a dynamic gate reassignment framework. Żak et al. consider a fleet sizing problem in a road freight transportation company with a heterogeneous fleet as well as technical back-up facilities. The decision problem is formulated in terms of multiple objective mathematical programming based on queuing theory. Various technical and economic criteria and stakeholder interests are taken into account.

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 score0.664
Threshold uncertainty score0.369

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 communication0.0000.001
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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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

Citations4
Published2011
Admission routes3
Has abstractyes

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