Recent Developments in Intelligent Transportation Systems (ITSs), 2nd Edition

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Guest Editor
Laboratoire sur La Perception, les Interactions, les Comportements et la Simulation des Usagers de la Route et de la Rue (PICS-L), Université Gustave Eiffel, Marne la Vallée, France
Interests: intelligent transportation systems; simulators and vehicles modeling; nonlinear observation; nonlinear control
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Guest Editor
Department of Civil, Chemical, Environmental, and Materials Engineering, Alma Mater Studiorum Università di Bologna, Bologna, Italy
Interests: road maintenance; safety; road materials; skid resistance and surface characteristics of road pavement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITSs) use emerging technologies (information, sensing, computing, and communication technologies) to advance transportation safety, improve mobility and operating efficiency and reliability, enhance user productivity, maintain transportation sustainability, and reduce the environmental impact of the growing demand for travel. These technologies target the transportation infrastructure, vehicles, and travelers, as well as integrated applications among them. ITSs are also a key component of the movement towards connected and smart communities, which incorporate connected transportation and travelers to ensure that data, technologies, and applications are fully integrated with other systems across a community. Intelligent transportation systems cover all modes of transportation, including ground transportation such as private automobiles, commercial vehicles, and public transit, as well as rail, marine, and air.

For this Special Issue of Vehicles, entitled “Recent Developments in Intelligent Transportation Systems (ITSs), 2nd Edition”, we welcome interdisciplinary research involving vehicles and transportation infrastructure, as well as integrated applications combining the two. Topics include, but are not limited to, the following:

  • Automated vehicle (AV) technology;
  • Connected and automated vehicle (CAV);
  • Cooperative driving automation (CDA);
  • Vehicle to everything (V2X) technologies, including vehicle-to-pedestrian (V2P), vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I);
  • Intelligent traffic control systems and next-generation traffic management systems;
  • Intelligent commercial vehicle systems;
  • Advanced transit systems;
  • Privacy and security of ITS.

Dr. Hocine Imine
Dr. Claudio Lantieri
Guest Editors

Manuscript Submission Information

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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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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 (ITSs)
  • intelligent traffic control systems
  • intelligent commercial vehicle systems
  • advanced transit systems

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Related Special Issue

Published Papers (2 papers)

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Research

15 pages, 5371 KiB  
Article
Impact of In-Cab Alerts on Connected Truck Speed Reductions in Indiana
by Jairaj Desai, Enrique D. Saldivar-Carranza, Rahul Suryakant Sakhare, Jijo K. Mathew and Darcy M. Bullock
Vehicles 2024, 6(4), 1857-1871; https://doi.org/10.3390/vehicles6040090 - 31 Oct 2024
Viewed by 551
Abstract
Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors [...] Read more.
Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors in Indiana from April–June 2024. Approximately 20,000 alerts were analyzed, with 92% being Congestion alerts and 8% being Dangerous Slowdown alerts. Observations showed that 15% of trucks lowered their speeds by at least 5 mph 30 s after receiving a Congestion alert, while 21% of trucks reduced their speeds by at least 5 mph 30 s after receiving a Dangerous Slowdown alert. The analysis also showed that a majority of Congestion alerted trucks encountered slow-speed traffic about 3 min after receiving an alert, while a majority of Dangerous Slowdown alerted drivers had traveled through the zone of slow speeds 2 min after receiving the alert. Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study reports that 43% of trucks that received Dangerous Slowdown alerts never reduced their speed below 45 mph. The paper concludes that it is important to converge on a shared vision for these performance measures so that public agencies, in-cab alert providers, and trucking companies can agilely improve these systems and increase driver confidence in the alerts. Full article
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19 pages, 3687 KiB  
Article
Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy
by Athulya Sundaresan Geetha, Mujadded Al Rabbani Alif, Muhammad Hussain and Paul Allen
Vehicles 2024, 6(3), 1364-1382; https://doi.org/10.3390/vehicles6030065 - 10 Aug 2024
Cited by 2 | Viewed by 4801
Abstract
Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as [...] Read more.
Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems. Full article
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