Transportation and Traffic Engineering

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 577

Special Issue Editors


E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: intelligent ship navigation theory and technology; intelligent maritime support technology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42100 Reggio Emilia, Italy
Interests: combinatorial optimization; operations research; machine learning; artificial intelligence; logistics; heuristic algorithms; exact algorithms
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor Assistant
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
Interests: intelligent surface vessel path following; multi-vessel formation collaboration; deep reinforcement learning

Special Issue Information

Dear Colleagues,

Against the backdrop of accelerated social progress and rapid technological innovation, the transportation industry is undergoing a profound transformation driven by digitization, intelligence, and sustainable development. To address escalating transportation demands and meet the goals of higher service quality, operational efficiency, and reduced carbon emissions, it is imperative to leverage advanced computational methodologies—particularly algorithmic innovation in artificial intelligence, optimization, data analytics, and autonomous systems—to reshape traditional transportation frameworks.

This Special Issue, “Transportation and Traffic Engineering, aims to build a high-level academic exchange platform for researchers, industry practitioners, and regulatory authorities worldwide. Integrating algorithmic theory with practical engineering, this issue adopts an interdisciplinary and forward-looking perspective, covering a broad spectrum of transportation domains including aviation, maritime, rail, and low-altitude traffic. It converges expertise from control engineering, machine learning, optimization theory, navigation technology, policy formulation, urban planning, and environmental science to present comprehensive insights into the latest theoretical advancements, algorithmic breakthroughs, and practical engineering solutions.

Interested topics include, but are not limited to the following:

  • Algorithmic foundations of intelligent transportation systems, including routing algorithms, scheduling optimization, multi-agent coordination, and large-scale traffic flow computation;
  • Autonomous transportation systems (autonomous ships, unmanned aerial vehicles, self-driving vehicles): decision-making algorithms, perception and sensor fusion methods, cooperative control algorithms, and human–machine interaction modeling;
  • Low-altitude traffic safety and management, including airspace allocation algorithms, real-time deconfliction models, infrastructure coordination, and algorithmic emergency response frameworks;
  • Sustainable aviation transportation, including flight trajectory optimization, air traffic flow management algorithms, and data-driven approaches to balancing scalability, efficiency, and environmental impact;
  • Maritime traffic engineering, including ship navigation algorithms, collision-avoidance models, multi-ship collaborative decision-making, and compliance automation with COLREGs/MASS frameworks;
  • Traffic safety, risk assessment, and early-warning systems, such as predictive modeling, anomaly detection algorithms, probabilistic risk forecasting, and emergency response optimization;
  • Green and low-carbon transportation technologies, including energy-efficient routing, electric vehicle charging optimization, alternative energy utilization planning, and environmental impact assessment algorithms.

Prof. Dr. Yong Ma
Prof. Dr. Roberto Montemanni
Guest Editors

Dr. Yujiao Zhao
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent transportation algorithms
  • autonomous and multi-agent systems
  • traffic optimization and scheduling
  • data-driven traffic safety and risk modeling
  • sustainable and low-carbon transportation systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

42 pages, 4153 KB  
Article
Hierarchical Reconciliation of Fifty-One Years of Highway–Rail Grade Crossing Data with Verified Multistage Inference
by Raj Bridgelall
Algorithms 2026, 19(4), 282; https://doi.org/10.3390/a19040282 - 3 Apr 2026
Viewed by 209
Abstract
Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a transparent nine-stage reconciliation [...] Read more.
Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a transparent nine-stage reconciliation pipeline applied to 51 years (1975–2025) of national HRGC incident data from the Federal Railroad Administration Form 57 and Form 71 datasets. The hierarchical pipeline integrated deterministic alignment and multistage inference methods to produce an audited, geographically consistent dataset. The study formalizes four longitudinal county-level cumulative exposure indices that characterize spatiotemporal patterns of incident concentration relative to static population and infrastructure denominators. These metrics include accumulated incidents per million population (AIPM), accumulated incidents per crossing (AIPC), crossings per million population (CPM), and crossings per 100 square miles (CPHSM). All four metrics exhibited pronounced right-skewness: AIPM, CPM, and CPHSM approximated exponential forms, and AIPC approximated a log-normal form. Statistical tests detected statistically significant tail deviations in three metrics; CPM did not reject the exponential fit at conventional significance levels. Spatial analysis shows coherent regional concentration in incident rates in the Central Plains and lower Mississippi corridors. The national time series exhibits a late-1970s plateau, sustained exponential decline beginning around 1980, and stabilization but persistent incident rates after 2001. Population-normalized AIPM remained statistically indistinguishable between the reconciled and record-dropped datasets; however, crossing-based metrics changed materially when reconstructing denominators from the reconciled crossing universe. Statistical comparisons confirmed that incident-only denominators introduced substantial measurement bias in local risk assessment. State-level rank reversals persisted even when omnibus distributional tests failed to reject equality. By formalizing multistage data cleaning and quantifying its analytical impact over an unprecedented longitudinal horizon, this study establishes denominator integrity and geographic reconciliation as prerequisites for valid HRGC exposure assessment and provides a framework for future predictive modeling. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
Show Figures

Graphical abstract

Back to TopTop