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Application of Artificial Intelligence in Traffic Management
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Application of Artificial Intelligence in Traffic Management

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 5909

Special Issue Editors

Dr. Weihao Ma

E-Mail Website
Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan, China
Interests: green and intelligent shipping; intelligent traffic control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongfang Ma

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Zhoushan, China
Interests: intelligent traffic control; marine data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue calls for original research and review articles focusing on the transformative role of Artificial Intelligence (AI) in addressing the challenges and complexities of modern traffic management systems. AI technologies are transforming how transportation networks operate, offering innovative solutions to enhance efficiency, safety, and sustainability. This Special Issue aims to explore (i) the development and application of advanced AI technology in optimizing and controlling road and waterway transportation, (ii) the integration of AI with intelligent transportation systems (ITSs) to improve decision making and real-time traffic operations, and (iii) AI’s potential in promoting sustainable, intelligent, and resilient urban transportation systems, as well as ocean-going transportation systems.

This Special Issue will complement the existing literature by providing a comprehensive, cross-sectoral perspective on AI applications in waterway and road traffic management. The topics of interest include, but are not limited to, the following:

  • AI-driven traffic forecasting and congestion management;
  • AI-based systems for sustainable transportation planning;
  • AI in traffic safety analysis and accident prevention;
  • AI in ocean-going vessels’ decision making;
  • AI in maritime management;
  • Big data analytics for shipping and traffic monitoring.

Dr. Weihao Ma
Prof. Dr. Dongfang Ma
Guest Editors

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. Big Data and Cognitive Computing 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

  • artificial intelligence
  • traffic management
  • maritime management
  • intelligent transportation systems
  • adaptive traffic control
  • traffic forecasting and optimization
  • ocean-going vessel

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

Published Papers (3 papers)

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Research

21 pages, 2326 KB  
Article
Highway Accident Hotspot Identification Based on the Fusion of Remote Sensing Imagery and Traffic Flow Information
by Jun Jing, Wentong Guo, Congcong Bai and Sheng Jin
Big Data Cogn. Comput. 2025, 9(11), 283; https://doi.org/10.3390/bdcc9110283 - 10 Nov 2025
Viewed by 1153
Abstract
Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this [...] Read more.
Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Traffic Management)
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34 pages, 3941 KB  
Article
Ship Typhoon Avoidance Route Planning Method Under Uncertain Typhoon Forecasts
by Zhengwei He, Junhong Guo, Weihao Ma and Jinfeng Zhang
Big Data Cogn. Comput. 2025, 9(6), 143; https://doi.org/10.3390/bdcc9060143 - 23 May 2025
Cited by 1 | Viewed by 2135
Abstract
Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based [...] Read more.
Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based on the distribution of historical typhoon data within the radius of seven-level winds and the distance between the ship and the typhoon. Subsequently, the minimum safe distance is quantified, and a multi-objective ship route optimization model for typhoon avoidance is established. A three-dimensional multi-objective ant colony algorithm is designed to solve this model. Finally, a typhoon avoidance simulation experiment is conducted using Typhoon TAMRI and a classic route in the South China Sea as a case study. The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. This model can serve as a reference for shipping companies in formulating typhoon avoidance strategies. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Traffic Management)
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20 pages, 3787 KB  
Article
Joint Optimization of Route and Speed for Methanol Dual-Fuel Powered Ships Based on Improved Genetic Algorithm
by Zhao Li, Hao Zhang, Jinfeng Zhang and Bo Wu
Big Data Cogn. Comput. 2025, 9(4), 90; https://doi.org/10.3390/bdcc9040090 - 8 Apr 2025
Cited by 1 | Viewed by 1638
Abstract
Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study [...] Read more.
Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO2, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Traffic Management)
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