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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 7293

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 (10 papers)

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Research

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20 pages, 1144 KB  
Article
Research on the Analysis of Influential Factors of Short-Period Passenger Flow of Urban Rail Transit Based on Spatio-Temporal Heterogeneity
by Minlei Qian, Lin Cheng and Jianan Sun
Systems 2025, 13(11), 985; https://doi.org/10.3390/systems13110985 - 4 Nov 2025
Abstract
Urban Rail Transit (URT), as an important part of the modern urban transportation system, undertakes a large number of daily commuter passenger flow transportation needs. In this context, the in-depth analysis of influential factors of URT passenger flow has become an important issue [...] Read more.
Urban Rail Transit (URT), as an important part of the modern urban transportation system, undertakes a large number of daily commuter passenger flow transportation needs. In this context, the in-depth analysis of influential factors of URT passenger flow has become an important issue in transportation management and optimization. This paper selects 13 POI (Point of Interest) types and the surrounding demographic data as the independent variables, and constructs a multi-scale spatio-temporal geographically weighted regression (MGTWR) model with the daily morning peak inbound traffic of the URT station as the dependent variable. The results of the study show that the positive effect of the business and residential variables on the URT morning peak inbound passenger flow is the most significant, reflecting the fact that the increase in these variables promotes the morning peak inbound passenger flow; relatively speaking, the scenic spot variables have a negative effect on the URT morning peak inbound passenger flow, indicating that the increase in these variables inhibits the morning peak inbound passenger flow. In addition, the corporate variables have a negative effect on the morning peak inbound passenger flow, and the company variables have a negative effect on the daily peak inbound passenger flow of URT. URT morning peak inbound passenger flow is non-stationary, i.e., the degree of its influence fluctuates greatly in different spatial and temporal scales. In order to further understand these influence mechanisms, this paper conducts an in-depth analysis of the spatio-temporal characteristics of the above three types of variables, revealing the influence of their spatio-temporal heterogeneity on URT passenger flow. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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33 pages, 2891 KB  
Article
Charging Decision Optimization Strategy for Shared Autonomous Electric Vehicles Considering Multi-Objective Conflicts: An Integrated Solution Process Combining Multi-Agent Simulation Model and Genetic Algorithm
by Shasha Guo, Xiaofei Ye, Shuyi Pei, Xingchen Yan, Tao Wang, Jun Chen and Rongjun Cheng
Systems 2025, 13(10), 921; https://doi.org/10.3390/systems13100921 - 20 Oct 2025
Viewed by 272
Abstract
There is a lack of systematic research on the behavioral design of charging decision-making for Shared Autonomous Electric Vehicles (ASEVs), and the thresholds of “when to charge and where to charge” have not been clarified. Therefore, this paper investigates the optimization of charging [...] Read more.
There is a lack of systematic research on the behavioral design of charging decision-making for Shared Autonomous Electric Vehicles (ASEVs), and the thresholds of “when to charge and where to charge” have not been clarified. Therefore, this paper investigates the optimization of charging decisions of SAEVs and the impact of different decision-making objectives to provide theoretical support and practical guidance for intelligent operation. A multi-agent simulation model (which accurately simulates complex interaction systems) is constructed to simulate the operation and charging behavior of SAEVs. Four charging decision optimization objective functions are defined, and a weighted multi-objective optimization method is adopted. A comprehensive solution process combining the multi-agent simulation model and genetic algorithm (efficiently solving complex objective optimization problems) is applied to approximate the global optimal solution among 35 scenarios through 100 iterative runs. In this paper, factors such as passenger demand (e.g., average remaining battery power, demand response time) and operator demand (e.g., empty vehicle mileage, charging cost) are considered, and the impacts of different objectives and decision variables are analyzed. The optimization results show that (1) when a single optimization objective is selected, minimizing the total charging cost effectively balances the overall fleet operation; (2) there are trade-offs between different objectives, such as the conflict between the remaining battery power and charging cost, and the balance between the demand response time and the empty vehicle mileage; and (3) in order to satisfy the operational requirements, the weight distribution, charging probability, stopping probability, and recommended battery power should be adjusted. In conclusion, this study provides optimal charging decision strategies for the intelligent operation of SAEVs in different scenarios, which can optimize target weights and charging parameters, and achieve dynamic, balanced fleet management. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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17 pages, 2744 KB  
Article
Adaptive Deployment of Fixed Traffic Detectors Based on Attention Mechanism
by Wenzhi Zhao, Ting Wang, Guojian Zou, Honggang Wang and Ye Li
Systems 2025, 13(10), 887; https://doi.org/10.3390/systems13100887 - 9 Oct 2025
Viewed by 341
Abstract
In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation [...] Read more.
In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation points through an end-to-end attention mechanism to support full-map traffic state estimation. Distinct from conventional fixed deployment strategies, DPSM provides an adaptive detector configuration that, under the same number of loop sensors, achieves significantly higher estimation accuracy by intelligently optimizing their placement. Specifically, the module takes normalized spatial and temporal information as input and generates an attention-based distribution to identify critical traffic flow readings, which are subsequently fed into various backbone prediction models, including fully connected networks, convolutional neural networks, and long short-term memory networks. Experiments on the real-world NGSIM-US101 dataset demonstrate that three variants—DPSM-NN, DPSM-CNN, and DPSM-LSTM—consistently outperform their corresponding baselines, with notable robustness under sparse observation scenarios. These results highlight the advantage of adaptive detector placement in maximizing the utility of limited sensors, effectively mitigating information loss from sparse deployments and offering a cost-efficient, scalable solution for urban traffic monitoring and control. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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19 pages, 2884 KB  
Article
Real-Time Risk Identification of Rear-End Conflicts at Unsignalized Intersections
by Hussain A. Nasr, Jieling Jin, Helai Huang and Hala A. Eljailany
Systems 2025, 13(9), 827; https://doi.org/10.3390/systems13090827 - 20 Sep 2025
Viewed by 535
Abstract
Rear-end collisions at unsignalized intersections remain a persistent issue in urban traffic environments, particularly at stop-controlled junctions. This study develops a real-time predictive model aimed at identifying potential rear-end conflicts, employing Deep & Cross Network Version 2 (DCNV2) to improve prediction accuracy. The [...] Read more.
Rear-end collisions at unsignalized intersections remain a persistent issue in urban traffic environments, particularly at stop-controlled junctions. This study develops a real-time predictive model aimed at identifying potential rear-end conflicts, employing Deep & Cross Network Version 2 (DCNV2) to improve prediction accuracy. The methodology comprises three main components: data acquisition, model development, and interpretability analysis. Real-time vehicle trajectory data such as speed, inter-vehicle distance, and interaction behavior are collected and preprocessed before being analyzed using the DCNV2 model to uncover patterns associated with conflict risk. The model integrates cross-feature interactions to enhance predictive performance. Evaluation metrics, including accuracy, recall, and area under the curve (AUC), demonstrate that DCNV2 outperforms conventional classifiers such as logistic regression and support vector machines. To further evaluate model interpretability, SHapley Additive exPlanations (SHAP) are applied, revealing that short following distances, large speed differentials, and high traffic volumes on major roads are primary contributors to rear-end conflict risk. The findings provide actionable insights to inform proactive traffic safety strategies, particularly in urban areas where limited signalization or manual control exposes drivers to increased uncertainty. This predictive framework supports the development of real-time safety interventions and contributes to more effective risk mitigation at critical locations within the traffic network. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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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 546
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 677
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 441
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 769
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 481
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 2055
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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