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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 January 2026 | Viewed by 5647

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Special Issue Editors

Prof. Dr. Yuchuan Du

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Guest Editor

School of Transportation Engineering, Tongji University, Shanghai 200092, China
Interests: traffic holographic perception and intelligent computing; Intelligent Transportation System (ITS); transportation economic analysis; transport infrastructure management system
Special Issues, Collections and Topics in MDPI journals

Dr. Yu Shen

E-Mail Website
Guest Editor

Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai, China
Interests: shared mobility; public transit; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Chenglong Liu

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Guest Editor

Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai, China
Interests: smart infrastructures; transportation digitalization; smart highway; applications of AI techniques in transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a significant surge in interest surrounding the integration of AI and smart sensors within Intelligent Transportation Systems (ITS). These technologies present transformative opportunities for enhancing traffic management, safety, and efficiency in urban environments. Smart sensors equipped with advanced AI algorithms enable real-time data collection and analysis, facilitating improved decision-making and predictive analytics. As cities strive for smarter infrastructure, the convergence of AI and sensor technology offers innovative solutions for congestion management, vehicle-to-everything (V2X) communication, and autonomous vehicle navigation.

This Special Issue aims to compile original research and review articles focused on the latest advancements, applications, challenges, and future directions in the realm of AI and smart sensors for ITS.

Potential topics include, but are not limited to, the following:

AI-driven traffic prediction models;
Smart sensor networks for real-time traffic monitoring;
Smart sensors for transportation infrastructures;
Vehicle-to-Infrastructure (V2I) communication;
Real-time data analytics;
Sensor fusion technologies.

Prof. Dr. Yuchuan Du
Dr. Yu Shen
Dr. Chenglong Liu
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. Sensors 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 2600 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 (ITS)
Artificial intelligence (AI)
smart sensors
Vehicle-to-Infrastructure (V2I) communication

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Published Papers (7 papers)

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Research

22 pages, 1566 KiB

Open AccessArticle

Opportunistic Allocation of Resources for Smart Metering Considering Fixed and Random Wireless Channels

by Christian Jara, Juan Inga and Esteban Inga

Sensors 2025, 25(8), 2570; https://doi.org/10.3390/s25082570 - 18 Apr 2025

Viewed by 148

Abstract

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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21 pages, 2174 KiB

Open AccessArticle

Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals

by Seifeldeen Eteifa, Amr Shafik, Hoda Eldardiry and Hesham A. Rakha

Sensors 2025, 25(6), 1664; https://doi.org/10.3390/s25061664 - 7 Mar 2025

Viewed by 1733

Abstract

Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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17 pages, 3343 KiB

Open AccessArticle

Comparative Analysis of YOLO Series Algorithms for UAV-Based Highway Distress Inspection: Performance and Application Insights

by Ziyi Yang, Xin Lan and Hui Wang

Sensors 2025, 25(5), 1475; https://doi.org/10.3390/s25051475 - 27 Feb 2025

Viewed by 843

Abstract

Established unmanned aerial vehicle (UAV) highway distress detection (HDD) faces the dual challenges of accuracy and efficiency, this paper conducted a comparative study on the application of the YOLO (You Only Look Once) series of algorithms in UAV-based HDD to provide a reference for the selection of models. YOLOv5-l and v9-c achieved the highest detection accuracy, with YOLOv5-l performing well in mean and classification detection precision and recall, while YOLOv9-c showed poor performance in these aspects. In terms of detection efficiency, YOLOv10-n, v7-t, and v11-n achieved the highest levels, while YOLOv5-n, v8-n, and v10-n had the smallest model sizes. Notably, YOLOv11-n was the best-performing model in terms of combined detection efficiency, model size, and computational complexity, making it a promising candidate for embedded real-time HDD. YOLOv5-s and v11-s were found to balance detection accuracy and model lightweightness, although their efficiency was only average. When comparing t/n and l/c versions, the changes in the backbone network of YOLOv9 had the greatest impact on detection accuracy, followed by the network depth_multiple and width_multiple of YOLOv5. The relative compression degrees of YOLOv5-n and YOLOv8-n were the highest, and v9-t achieved the greatest efficiency improvement in UAV HDD, followed by YOLOv10-n and v11-n. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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16 pages, 3319 KiB

Open AccessArticle

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

by Rohit Bokade and Xiaoning Jin

Sensors 2025, 25(5), 1302; https://doi.org/10.3390/s25051302 - 20 Feb 2025

Cited by 1 | Viewed by 564

Abstract

Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face challenges such as slow simulation speeds and convoluted, difficult-to-maintain codebases. To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. PyTSC integrates multiple simulators, such as SUMO and CityFlow, and offers a streamlined API, enabling researchers to explore a broad spectrum of MARL approaches efficiently. PyTSC accelerates experimentation and provides new opportunities for advancing intelligent traffic management systems in real-world applications. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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25 pages, 7195 KiB

Open AccessArticle

A Comprehensive Framework for Evaluating Cycling Infrastructure: Fusing Subjective Perceptions with Objective Data

by Kefei Tian, Yifan Zheng, Zhongyu Sun, Zishun Yin, Kai Zhu and Chenglong Liu

Sensors 2025, 25(4), 1168; https://doi.org/10.3390/s25041168 - 14 Feb 2025

Viewed by 662

Abstract

As cities increasingly prioritize green and low-carbon transportation, the development of effective cycling infrastructure has become essential for alleviating traffic congestion and reducing environmental impacts. However, the service quality of bike lanes remains inadequate. To address this gap, this study proposes a multi-data-fusion framework for evaluating bike lane “cycling friendliness”, integrating subjective perceptions with objective metrics. The framework combines survey-based subjective data with digital measurements to enable rapid, large-scale assessments that align with user expectations. Tailored evaluation models are developed based on revealed preference (RP) survey analysis to account for variations among target user groups. Key factors such as road roughness, motor vehicle encroachment, cycling-friendly amenities, and roadside scenery are quantitatively assessed using vibration analysis and computer vision techniques. Validation results reveal a strong correlation between model predictions and subjective evaluations, demonstrating the framework’s reliability and effectiveness. This approach offers a scalable, data-driven tool for optimizing bike route selection and guiding infrastructure upgrades, thus advancing urban cycling transportation. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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27 pages, 7360 KiB

Open AccessArticle

Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

by Amr K. Shafik and Hesham A. Rakha

Sensors 2025, 25(3), 830; https://doi.org/10.3390/s25030830 - 30 Jan 2025

Viewed by 1010

Abstract

This paper introduces a two-stage adaptive Kalman filter algorithm to estimate and predict traffic states required for real-time traffic signal control. Leveraging probe vehicle trajectory and upstream detector data, turning movement (TM) counts in the vicinity of signalized intersections are estimated in the first stage, while the upstream approach density and queue sizes are estimated in the second stage. The proposed approach is evaluated using drone-collected and simulated data from a four-legged signalized intersection in Orlando, Florida. The performance of the two-stage approach is quantified relative to the baseline estimation without a Kalman filter. The results show that the Kalman filter is effective in enhancing traffic state estimates at various market penetration levels, where the filter both improves the estimation accuracy over the baseline case and provides reliable state predictions. In the first stage, the standard deviation (SD) in TM estimates improves by up to 50% compared to the estimates provided by the sole use of probe vehicle headings. The proposed approach also provides predictions with a minimal SD of 92.8 veh/h at a 5% level of market penetration. In the second stage, the proposed queue size estimation method results in an enhancement to the queue size estimation of up to 32.8% compared to the estimates obtained from the baseline approach. In addition, the estimated traffic density is enhanced by up to 18.5%. The proposed two-stage approach demonstrates the capability of providing reliable turning movement predictions across varying levels of market penetration. This highlights the readiness of this approach for practical application in real-time traffic signal control systems. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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21 pages, 5852 KiB

Open AccessArticle

Study on the Attribute Characteristics of Road Cracks Detected by Ground-Penetrating Radar

by Shili Guo, Mingyu Yu, Zhiwei Xu, Guanghua Yue, Wencai Cai and Pengfei Tian

Sensors 2025, 25(3), 595; https://doi.org/10.3390/s25030595 - 21 Jan 2025

Viewed by 788

Abstract

Cracks are a common form of road distress that can significantly impact pavement integrity. Accurate detection of the attribute characteristics of cracks, including the type, location (top and bottom), width, and orientation, is crucial for effective repair and treatment. This study combines numerical simulations with filed data to investigate how the amplitudes of ground-penetrating radar (GPR) early-time signals (ETSs) vary with changes in the crack top and width, as well as how variations in the crack bottom impact radar reflected wave amplitude. The results show that when GPR ETSs are mixed with diffracted waves from the crack top, the amplitude change percentage of the ETS at the crack top exhibits a pronounced ‘∨’-shaped dip, which provides a clearer indication of the crack top. Furthermore, a positive correlation exists between crack width and the amplitude change percentage, offering a theoretical basis for quantitatively estimating crack width. On the reflected wave originating from the interface between the semi-rigid base and the subgrade, a pronounced ‘∧’-shaped dip is observed in the trough amplitude change percentage of the reflected wave at the crack bottom. For cracks of the same width, the amplitude of the ‘∧’ vertex from reflective cracks is approximately three times greater than that from fatigue cracks. This discrepancy helps identify the crack bottom and quantitatively diagnose their types. The line connecting the vertices of the ‘∨’ and ‘∧’ shapes indicate the crack’s orientation. Accurate diagnosis of crack properties can guide precise, minimally invasive treatment methods, effectively repairing road cracks and extending the road’s service life. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems)

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