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Algorithms
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Transportation and Traffic Engineering
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Transportation and Traffic Engineering

A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2975

Editors

Prof. Dr. Yong Ma

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
Prof. Dr. Roberto Montemanni

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
Dr. Yujiao Zhao

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-anonymized 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

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

Published Papers (6 papers)

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Research

41 pages, 15921 KB  
Article
A Mobility Priority Index for Data-Driven National Bridge Preservation Prioritization
by Raj Bridgelall
Algorithms 2026, 19(9), 791; https://doi.org/10.3390/a19090791 - 15 Sep 2026
Viewed by 239
Abstract
Bridge and pavement management systems typically evaluate structural condition and roadway performance separately, limiting network-level consideration of infrastructure condition and traffic exposure. This study develops the Mobility Priority Index (MPI), a relative composite screening measure that integrates pavement roughness, bridge deck condition, current [...] Read more.
Bridge and pavement management systems typically evaluate structural condition and roadway performance separately, limiting network-level consideration of infrastructure condition and traffic exposure. This study develops the Mobility Priority Index (MPI), a relative composite screening measure that integrates pavement roughness, bridge deck condition, current traffic exposure, and projected traffic growth. A transparent and transferable workflow spatially linked the Highway Performance Monitoring System and National Bridge Inventory and applied sequential quality screening to 79,595 open, unrestricted mainline National Highway System bridges in the contiguous United States. The MPI was descriptively characterized using distributional analysis; spatial patterns and specification sensitivity were examined using local Getis–Ord Gi* hotspot analysis and eight alternative formulations. The baseline identified 20,621 bridges (25.9%) as nominally significant exploratory hotspots, concentrated mainly in metropolitan regions and high-demand corridors. Across the alternative specifications, bridge-rank correlations ranged from 0.807 to 0.965, whereas hotspot-set Jaccard similarities ranged from 0.446 to 0.801, indicating greater sensitivity in hotspot membership than in overall rankings. The MPI does not measure realized delay, congestion, reliability, user cost, or investment benefit. Instead, it provides a nationally scalable screening method that identifies bridges and regions warranting further engineering and operational evaluation during preservation planning. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
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32 pages, 8453 KB  
Article
Joint Location and Capacity Optimization of Electric Vehicle Charging and Battery-Swapping Stations Using Random Forest Surrogate-Assisted NSGA-III
by Zihan Li, Bo Yang, Huanming Zhang, Xiangyu Zhao, Yuanweiji Hu, Junyu Liang and Xuehao He
Algorithms 2026, 19(9), 783; https://doi.org/10.3390/a19090783 - 10 Sep 2026
Viewed by 189
Abstract
The increasing penetration of electric vehicles (EVs) creates new challenges for coordinated planning of charging and battery-swapping infrastructure. This study aims to develop a joint location and capacity planning framework for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs) in [...] Read more.
The increasing penetration of electric vehicles (EVs) creates new challenges for coordinated planning of charging and battery-swapping infrastructure. This study aims to develop a joint location and capacity planning framework for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs) in an electric–traffic coupled system, considering infrastructure cost, user service requirements, and distribution-network performance. A multi-objective planning model is established based on spatial-temporal energy replenishment demand and traffic–grid coupling. To improve computational efficiency, a random forest surrogate-assisted NSGA-III (RF-SA-NSGA-III) is proposed, in which expensive user-service and voltage-related objective evaluations are selectively approximated by random forest models, combined with periodic true-model correction and final verification. Case studies show that the proposed method obtains competitive Pareto-optimal solutions and replaces 89.38% of expensive evaluations, reducing computational time from 45,706.3 s to 4689.4 s. The framework provides practical support for coordinated EVCS–EVSS siting and capacity allocation while balancing investment, user service quality, and voltage performance. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
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32 pages, 1073 KB  
Article
An Integrated Scheduling Model for Airport Apron-Bus Drivers Under Stochastic Demand
by Yi Zheng, Jun Xu, Huan Xia and Hao Tang
Algorithms 2026, 19(8), 651; https://doi.org/10.3390/a19080651 - 6 Aug 2026
Viewed by 336
Abstract
Airport apron buses, which shuttle passengers between terminal gates and remotely parked aircraft, are vital for efficient ground operations. However, flight delays and disruptions make demand for apron-bus services uncertain and time-varying, creating substantial challenges for planning driver capacity and work schedules. To [...] Read more.
Airport apron buses, which shuttle passengers between terminal gates and remotely parked aircraft, are vital for efficient ground operations. However, flight delays and disruptions make demand for apron-bus services uncertain and time-varying, creating substantial challenges for planning driver capacity and work schedules. To tackle this challenge, we develop an Integrated Stochastic-Flexible Planning Model (ISFPM), formulated as a mixed-integer linear program (MILP), that simultaneously optimizes workforce sizing, duty scheduling, and roster assignment for apron-bus drivers. The objective is to minimize the sum of labor costs and the expected penalty for understaffing. This penalty is evaluated under the assumption that driver demand in each period follows a Poisson-binomial distribution, which is derived from probabilistic models of flight delays. For computational efficiency, we reformulate the expected penalty term using continuity-corrected normal approximations based on the cumulative distribution function (CDF). Furthermore, we incorporate practical workforce flexibility features, including hourly-granularity duty start times and heterogeneous workday patterns across roster groups. The computational study is based on Beijing Capital International Airport and accompanied by deidentified replication materials. Across 100 materialized baseline scenarios, the ISFPM uses 148 drivers instead of the 175-driver deterministic fixed-shift benchmark, reducing average management cost by 21.9% and passenger waiting time by 85.6%. Across 18 representative-day scenarios with correlated and severe disruptions, it reduces mean management cost by 15.8% and passenger waiting time by 53.6%. The disruption-scenario analysis also indicates that deterministic flexible staffing attains the lowest waiting time at a higher cost, while common apron travel-time shocks reduce the service advantage of the ISFPM. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
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24 pages, 14372 KB  
Article
An Attention-Enhanced CNN with Explainable AI for Driver Behavior Detection in Intelligent Transportation Systems
by Abdullah Al Mamun, Md Shahidul Islam Shabuz, Md Nahidur Rahaman, Khawja Imran Masud and Md. Biddut Hossain
Algorithms 2026, 19(8), 615; https://doi.org/10.3390/a19080615 - 23 Jul 2026
Viewed by 420
Abstract
Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism [...] Read more.
Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism to ensure that it captures local spatial features as well as long-range dependencies in the activities of the driver. The State Farm Distracted Driver dataset is experimented with using stratified 5-fold cross-validation. The proposed MHA-CNN model has a mean accuracy of 99.48% on all folds and an overall high levels of precision, recall, and F1-score in all ten distraction classes. Grad-CAM is used to produce attribution maps to improve interpretability by showing semantically important parts of the image, including hands, face, and mobile devices, and can give a visual explanation of why models make decisions. In addition, an ablation investigation is performed with k-fold validation to assess the value of the multi-head attention mechanism. Its model can run at 122 frames per second with 42.92 million parameters, showing that the suggested method is a reasonable tradeoff between accuracy, efficiency, and transparency and can be applied in real-time driver monitoring applications. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
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19 pages, 1348 KB  
Article
A Multi-Scenario Approach of Emergency Rescuer Training and Dispatching Integration with Knowledge Accumulation Function for Large-Scale Emergencies
by Zhe Wang, Mengqi Tao, Xinxin Zong and Xingyuan Kuang
Algorithms 2026, 19(6), 446; https://doi.org/10.3390/a19060446 - 1 Jun 2026
Viewed by 307
Abstract
In responses to large-scale emergencies, emergency rescuers often face inadequate professional competence and critical personnel shortages caused by decentralized management and insufficient specialized training, which compromise self-protection and rescue performance. The current literature largely treats training and dispatching as isolated processes, overemphasizes personnel [...] Read more.
In responses to large-scale emergencies, emergency rescuers often face inadequate professional competence and critical personnel shortages caused by decentralized management and insufficient specialized training, which compromise self-protection and rescue performance. The current literature largely treats training and dispatching as isolated processes, overemphasizes personnel allocation while underrating training evaluation, and commonly assumes sufficient qualified rescuers, thus failing to resolve capability gaps and multi-scenario shortages. To bridge these research gaps, this paper develops a multi-scenario integrated approach for emergency rescuer training and dispatching with knowledge accumulation. The methodology integrates centralized pre-dispatch training and dynamic multi-scenario dispatching, establishes a training evaluation model based on knowledge accumulation and capability utility functions, adopts time-dependent task penalty variables to assess shortage impacts, and employs the SEVIR model for emergency medical demand prediction. A multi-objective optimization model is formulated and solved by particle swarm optimization (PSO) and the greedy algorithm for comparison. The contributions are threefold: (1) proposing a training–dispatching integration framework to break traditional separation; (2) realizing quantifiable training evaluation via knowledge accumulation; (3) validating the approach through emergency medical missions, showing that PSO achieves lower penalties and higher utility. This integrated method effectively boosts rescue capacity, mitigates shortage risks, and improves emergency response efficiency. Full article
(This article belongs to the Special Issue Transportation and Traffic Engineering)
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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
Cited by 4 | Viewed by 678
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)
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