--- name: transportation-research-part-b-methodological description: Use when targeting Transportation Research Part B (Methodological) or deciding whether a transportation manuscript fits this venue. Encodes the journal's methodological-flagship fit, the theoretical-contribution bar, the Part B vs. Part A/C/E routing, modeling-and-proof rigor, house style, official-submission re-check, and desk-reject heuristics. --- # Transportation Research Part B: Methodological (transportation-research-part-b-methodological) ## Journal positioning Transportation Research Part B (Methodological) is the Elsevier methodological flagship of the transportation research family, publishing work whose primary contribution is a **methodological or theoretical advance** in transportation modeling and analysis: traffic flow theory, network equilibrium and traffic assignment, transportation network design and optimization, travel-demand and discrete-choice modeling, transport economics methods, and freight/logistics modeling. The defining expectation is a generalizable method, model, or theorem — a new formulation, a proven property, a new estimator or algorithm with analytical justification — not an applied case study that uses existing methods. A well-executed empirical application with no methodological novelty belongs in Part A; this skill is a **fit / venue-selection / re-framing** tool. It does not replace the journal's current official author guidelines. Before submitting, re-check the live Transportation Research Part B Guide for Authors. ## When to trigger - The author names Part B for a transportation modeling, network, choice, or transport-economics manuscript and wants a fit/framing check. - A paper must be re-framed from "we applied a model to this city/dataset" into a generalizable methodological contribution with analytical results. - The author is deciding among Part B (methodological), Part A (policy/behavior), Part C (emerging technologies), and Part E (logistics/transportation economics applications). - The author needs Part B's modeling-rigor and proof expectations and its desk-reject heuristics. ## Scope & topic fit - Traffic flow theory: kinematic-wave and car-following models, macroscopic fundamental diagrams, network loading, with new analytical or modeling results. - Network equilibrium and traffic assignment: user/system equilibrium, dynamic traffic assignment, existence/uniqueness and convergence properties. - Transportation network design and optimization: bilevel/robust/stochastic formulations, exact and approximation algorithms with performance guarantees. - Travel-demand and discrete-choice modeling: new model structures, identification and estimation theory, behavioral econometrics for transportation. - Transport economics methods: congestion pricing, capacity and investment theory, mechanism design — when the contribution is methodological, not a policy case. - Freight, logistics, and supply-chain modeling when the advance is a formulation, algorithm, or analytical property rather than an industry case study. ## Method & evidence bar - The central object is a **method, model, or theorem** with a clear, generalizable contribution; analytical results (existence, uniqueness, optimality, convergence, identification) are stated and proven where claimed. - Assumptions must be explicit and reasonable; a result that holds only under assumptions that trivialize the problem is not a contribution. - Algorithms require complexity or convergence analysis, or rigorous computational evidence on benchmark instances, not a single illustrative run. - Econometric/choice contributions must address identification and estimation properties, not merely report coefficient estimates from one dataset. - Numerical experiments validate and illustrate the method; they support but never substitute for the analytical contribution. - Position precisely against the closest prior models/theorems: state what is new (weaker assumptions, broader network class, tighter bound, new identification). ## Structure & house style - Standard methodological-article structure: precise problem formulation, model/method development, analytical results (propositions/theorems with proofs), and numerical experiments; Part B publishes full-length methodological articles, so route applied or short pieces elsewhere and re-check current article types on the live guide. - The introduction motivates the methodological gap in the transportation literature, not the policy importance of a corridor or city. - Notation must be standard and consistent; the formulation is stated precisely before any result, and proofs appear in-text or in an appendix per current rules. - Figures and tables serve the method (convergence plots, sensitivity to network size, benchmark comparisons); the paper stands on its formulation and results. - Supplementary/appendix material carries long proofs and full computational details per the current policy. ## Official-submission checklist - Before giving submission-ready advice, read `../../resources/source-basis.md` and `../../resources/official-source-map.md`; start from the Elsevier anchors, then cite the current Transportation Research Part B Guide for Authors page you checked. - Search the live site for "Transportation Research Part B guide for authors" and follow the current Elsevier/Editorial Manager version; confirm you are targeting Part B (Methodological), not Part A/C/E. - Re-check article types, length expectations, and structured-abstract or highlights requirements if applicable. - Confirm data/code availability expectations for numerical experiments and any benchmark-instance sharing policy. - Re-check competing-interests, funding, author-contribution (CRediT), and AI-use disclosure requirements. - If the live official instructions conflict with this skill, the official instructions win. ## Pre-submission self-check - [ ] The contribution is a generalizable method/model/theorem, not an application of existing methods to one dataset. - [ ] Every analytical claim (existence/uniqueness/optimality/convergence/identification) has a complete, correct proof or rigorous justification. - [ ] Assumptions are explicit and non-trivializing, and the result's scope is clearly delimited. - [ ] Novelty is pinned to specific prior models/theorems (weaker assumptions / broader class / tighter bound / new identification). - [ ] Numerical experiments illustrate and validate but do not substitute for the analytical contribution. - [ ] The paper targets Part B specifically, not Part A/C/E, and notation/formulation is precise. ## Common desk-reject triggers - An applied case study that uses existing models with no methodological advance (a Part A fit). - An algorithm with no complexity/convergence analysis and only a single illustrative run. - A choice/econometric model reporting estimates from one dataset with no identification or estimation contribution. - Results stated without proofs, or proofs that are incomplete, incorrect, or rely on trivializing assumptions. - Scope mismatch: a pure operations-research, pure machine-learning, or technology-deployment paper with transportation only as a label. - Better framed for the technology-focused Part C or the logistics-applications Part E. ## Re-routing decision - Policy, behavior, or empirical analysis without methodological novelty → Transportation Research Part A. - Emerging-technology / sensing / data-driven ITS focus → Transportation Research Part C. - Logistics and transportation-economics applications → Transportation Research Part E. - Network optimization with no transportation object as the core → a dedicated operations-research venue. - General methodological breadth beyond this bundle → consult the natural-science routing slugs only if scope truly leaves engineering. ## Output format ```text [Fit] High / Medium / Low (one-line reason) [Target] Transportation Research Part B (Methodological) [Topic tags] <2–3 closest methodological subtopics> [Contribution type] new model / formulation / theorem / estimator / algorithm [Method/evidence] [Top risk] [Part check] B vs. A vs. C vs. E [Official items to re-check]
[Re-route suggestion] ```