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AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility
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AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 4370

Special Issue Editors

Prof. Dr. Rongjun Cheng

E-Mail Website
Guest Editor
Faculty of Maritime and Transportation, Ningbo University, Ningbo, China
Interests: autonomous driving; traffic demand forecasting; mixed traffic flow modelling and simulation; traffic state estimation; multimodal vehicle trajectory; deep learning
Dr. Xiaofei Ye

E-Mail Website
Guest Editor
Faculty of Maritime and Transportation, Ningbo University, Ningbo, China
Interests: transportation big data analysis; autonomous driving simulation model; parking planning and design; transportation and energy integration; emergency logistics; transportation safety analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Cong Zhai

E-Mail Website
Guest Editor
School of Transportation and Civil Engineering and Architecture, Foshan University, Foshan, China
Interests: modeling and simulation of complex traffic systems; intelligent network transportation

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into transportation systems has revolutionized the development of next-generation vehicles and mobility ecosystems. This Special Issue focuses on cutting-edge research leveraging AI to enhance the safety, efficiency, and sustainability of transportation systems. Topics include autonomous driving algorithms, AI-optimized traffic flow prediction, human–machine interactions in intelligent vehicles, energy management for electric/hybrid vehicles, digital healthcare engineering (DHE), and AI-enabled predictive maintenance for transport infrastructure. Emerging challenges such as edge computing for real-time decision-making, explainable AI in safety-critical scenarios, and the ethical implications of AI-driven mobility will also be explored. Submissions are encouraged to address multimodal transportation integration, including aerial drones, maritime vessels, and hyperloop systems, with an emphasis on system-level interoperability. Additionally, we welcome studies on digital twin frameworks, federated learning for distributed transportation networks, and AI applications in reducing carbon footprints. This Issue aims to bridge theoretical advancements with practical implementations, fostering discussions on regulatory frameworks and the societal acceptance of AI-powered transportation.

Prof. Dr. Rongjun Cheng
Dr. Xiaofei Ye
Dr. Cong Zhai
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Systems 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 2400 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 systems
  • trajectory prediction
  • ship performance calculation and ship path planning
  • crew health system
  • traffic accident reconstruction
  • digital twin technology
  • deep learning
  • intelligent parking systems
  • transportation and energy integration emergency logistics
  • application of large language models in the transportation field

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Published Papers (6 papers)

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Research

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25 pages, 3364 KB  
Article
Multi-Region Taxi Pick-Up Demand Prediction Based on Edge-GATv2-LSTM
by Jiawen Li, Zhengfeng Huang, Jinliang Li and Pengjun Zheng
Systems 2025, 13(8), 681; https://doi.org/10.3390/systems13080681 - 11 Aug 2025
Viewed by 235
Abstract
Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid [...] Read more.
Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid prediction framework, Edge-GATv2-LSTM, which integrates an edge-aware attention-based graph neural network (Edge-GATv2) with a temporal modeling component (LSTM). The framework not only models spatial interactions among regions via GATv2 and temporal evolution via LSTM but also incorporates edge features into the attention computation structure, jointly representing them with node features. This enables the model to perceive both node attributes and the strength of inter-regional relationships during attention weight calculation. Experiments are conducted based on real-world taxi order data from Ningbo City, and the results demonstrate that the adopted Edge-GATv2-LSTM model exhibits favorable performance in terms of pick-up demand prediction accuracy. Specifically, the model achieves the lowest RMSE and MAE of 3.85 and 2.86, respectively, outperforming all baseline methods and confirming its effectiveness in capturing spatiotemporal demand patterns. This research can provide decision-making support for taxi drivers, platform operators, and traffic management departments—for example, by offering a reference basis for optimizing taxi pick-up route planning when vehicles are unoccupied. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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17 pages, 26388 KB  
Article
City-Level Road Traffic CO2 Emission Modeling with a Spatial Random Forest Method
by Hansheng Jin, Dongyu Wu and Yingheng Zhang
Systems 2025, 13(8), 632; https://doi.org/10.3390/systems13080632 - 28 Jul 2025
Viewed by 402
Abstract
In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies [...] Read more.
In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO2 emissions. Another theoretical issue is concerned with the analytic method for zonal CO2 emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO2 emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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33 pages, 7555 KB  
Article
A Quasi-Bonjean Method for Computing Performance Elements of Ships Under Arbitrary Attitudes
by Kaige Zhu, Jiao Liu and Yuanqiang Zhang
Systems 2025, 13(7), 571; https://doi.org/10.3390/systems13070571 - 11 Jul 2025
Viewed by 281
Abstract
Deep-sea navigation represents the future trend of maritime navigation; however, complex seakeeping conditions often lead to unconventional ship attitudes. Conventional calculation methods are insufficient for accurately assessing hull performance under heeled or extreme trim conditions. Drawing inspiration from Bonjean curve principles, this study [...] Read more.
Deep-sea navigation represents the future trend of maritime navigation; however, complex seakeeping conditions often lead to unconventional ship attitudes. Conventional calculation methods are insufficient for accurately assessing hull performance under heeled or extreme trim conditions. Drawing inspiration from Bonjean curve principles, this study proposes a Quasi-Bonjean (QB) method to compute ship performance elements in arbitrary attitudes. Specifically, the QB method first constructs longitudinally distributed hull sections from the Non-Uniform Rational B-Spline (NURBS) surface model, then simulates arbitrary attitudes through dynamic waterplane adjustments, and finally calculates performance elements via sectional integration. Furthermore, an Adaptive Surface Tessellation (AST) method is proposed to optimize longitudinal section distribution by minimizing the number of stations while maintaining high geometric fidelity, thereby enhancing the computational efficiency of the QB method. Comparative experiments reveal that the AST-generated 100-station sections achieve computational precision comparable to 200-station uniform distributions under optimal conditions, and the performance elements calculated by the QB method under multi-attitude conditions meet International Association of Classification Societies accuracy thresholds, particularly excelling in the displacement and vertical center of buoyancy calculations. These findings confirm that the QB method effectively addresses the critical limitations of traditional hydrostatic tables, providing a theoretical foundation for analyzing damaged ship equilibrium and evaluating residual stability. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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20 pages, 1369 KB  
Article
Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control
by Rongjun Cheng, Haoli Lou and Qi Wei
Systems 2025, 13(6), 481; https://doi.org/10.3390/systems13060481 - 17 Jun 2025
Viewed by 495
Abstract
The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven [...] Read more.
The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. Due TV-MPC can be applied to any traffic congestion scenario and the dynamic modeling that considers driving style, can be easily transferred to other control algorithms. Thus, TV-MPC enable to represent typical control algorithms in mixed traffic flow. This study investigates the performance of TV-MPC under diverse disturbance characteristics and mixed platoons. Firstly, quantifying mixed traffic flow with different CAV penetration rates and platooning intensities by a Markov chain model. Secondly, by constructing evaluation indicators for micro-level operation of mixed traffic flow, this paper analyzed the impact of TV-MPC on the operation of mixed traffic flow through simulation. The results demonstrate that (1) CAV achieve optimal control at specific positions within mixed traffic flow; (2) higher CAV penetration enhances TV-MPC performance; (3) dispersed CAV distributions improve control effectiveness; and (4) TV-MPC excels in scenarios with significant disturbances. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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31 pages, 4745 KB  
Article
Effect of Pre-Trip Information in a Traffic Network with Stochastic Travel Conditions: Role of Risk Attitude
by Yun Yu, Shiteng Zheng, Yuankai Li, Huaqing Liu and Jianan Cao
Systems 2025, 13(6), 407; https://doi.org/10.3390/systems13060407 - 24 May 2025
Viewed by 370
Abstract
Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and [...] Read more.
Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient p. In the special case p=1, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition p>0, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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Review

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20 pages, 8834 KB  
Review
Human Digital Healthcare Engineering for Enhancing the Health and Well-Being of Seafarers and Offshore Workers: A Comprehensive Review
by Meng-Xuan Cui, Kun-Hou He, Fang Wang and Jeom-Kee Paik
Systems 2025, 13(5), 335; https://doi.org/10.3390/systems13050335 - 1 May 2025
Cited by 1 | Viewed by 1637
Abstract
With over 50,000 merchant vessels and nearly two million seafarers operating globally, more than 12,000 maritime incidents in the past decade underscore the urgent need for proactive safety measures to ensure the structural integrity of aging ships and safeguard the well-being of seafarers, [...] Read more.
With over 50,000 merchant vessels and nearly two million seafarers operating globally, more than 12,000 maritime incidents in the past decade underscore the urgent need for proactive safety measures to ensure the structural integrity of aging ships and safeguard the well-being of seafarers, who face harsh ocean environments in remote locations. The Digital Healthcare Engineering (DHE) framework offers a proactive solution to these challenges, comprising five interconnected modules: (1) real-time monitoring and measurement of health parameters, (2) transmission of collected data to land-based analytics centers, (3) data analytics and simulations leveraging digital twins, (4) AI-driven diagnostics and recommendations for remedial actions, and (5) predictive health analysis for optimal maintenance planning. This paper reviews the core technologies required to implement the DHE framework in real-world settings, with a specific focus on the well-being of seafarers and offshore workers, referred to as Human DHE (HDHE). Key technical challenges are identified, and practical solutions to address these challenges are proposed for each individual module. This paper also outlines future research directions to advance the development of an HDHE system, aiming to enhance the safety, health, and overall well-being of seafarers operating in demanding maritime environments. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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