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Applied Sciences
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Applications of Artificial Intelligence in Transportation Engineering
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Applications of Artificial Intelligence in Transportation Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 2235

Special Issue Editor

Dr. Roberto Carballedo

E-Mail Website
Guest Editor
Department of Information Technology, Electronics and Communication, University of Deusto, 48007 Bizkaia, Spain
Interests: artificial intelligence; optimization; vehicle routing problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative force in the field of transportation engineering, revolutionizing the way we conceptualize, plan, and execute mobility solutions. This Special Issue, entitled "Applications of Artificial Intelligence in Transportation Engineering", seeks to explore the multifaceted impact of AI on the design, operation, and sustainability of transportation systems. From intelligent traffic management and predictive maintenance to autonomous vehicles and route optimization, this collection aims to showcase cutting-edge research that elucidates the integration of AI in addressing the challenges and shaping the future of transportation.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • AI-Enabled Traffic Control Systems: Exploration of intelligent traffic control systems leveraging AI algorithms to enhance vehicular efficiency and flow.
  • Predictive Analytics for Transportation Infrastructure: Application of AI-driven predictive analytics to anticipate and address issues in transportation infrastructure, optimizing management and maintenance.
  • Autonomous Vehicles and Intelligent Navigation: Research on the integration of autonomous vehicles and intelligent navigation through AI algorithms.
  • Advanced Algorithms for Route Optimization: Utilization of advanced AI algorithms for efficient route optimization, reducing travel times and resource consumption.
  • Machine Learning in Traffic Pattern Analysis: Application of machine learning techniques to analyze traffic patterns and improve route planning.
  • Sustainable Transportation Solutions: Exploration of sustainable transportation solutions through intelligent technologies and AI-backed practices.
  • Integration of Robotics in Mobility: Investigation into how robotics integrates into transportation systems, enhancing automation and efficiency.
  • AI-Based Emergency Response Systems: Development of AI-based emergency response systems for critical situations in transportation.
  • Human–Machine Collaboration in Transportation Networks: Study of collaboration between humans and machines in transportation networks, addressing interoperability challenges.
  • Ethical and Regulatory Considerations in AI-Driven Transportation: Examination of ethical considerations and regulations in the use of AI to drive innovations in transportation.

Dr. Roberto Carballedo
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • AI-enabled traffic control systems
  • AI-driven transportation
  • intelligent transportation systems
  • traffic prediction
  • autonomous vehicles and intelligent navigation
  • route optimization
  • traffic pattern analysis
  • sustainable transportation

Published Papers (3 papers)

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Research

17 pages, 3504 KiB  
Article
ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting
by Zuhua Li, Siwei Wei, Haibo Wang and Chunzhi Wang
Appl. Sci. 2024, 14(10), 4130; https://doi.org/10.3390/app14104130 - 13 May 2024
Viewed by 266
Abstract
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby [...] Read more.
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Transportation Engineering)
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25 pages, 26268 KiB  
Article
Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction
by Jinghao Cao, Yang Li and Sidan Du
Appl. Sci. 2024, 14(7), 2795; https://doi.org/10.3390/app14072795 - 27 Mar 2024
Viewed by 492
Abstract
Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection [...] Read more.
Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Transportation Engineering)
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18 pages, 16034 KiB  
Article
Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles
by Dilshad Mohammed and Balázs Horváth
Appl. Sci. 2024, 14(1), 337; https://doi.org/10.3390/app14010337 - 29 Dec 2023
Cited by 2 | Viewed by 918
Abstract
As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights [...] Read more.
As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Transportation Engineering)
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