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 589

Special Issue 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

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  • 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 (1 paper)

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18 pages, 16034 KiB  
Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles
Appl. Sci. 2024, 14(1), 337; - 29 Dec 2023
Viewed by 447
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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