Intelligent Transportation Systems in Smart Cities

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4653

Special Issue Editors

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: information security; wireless networks; blockchain technology; digital forensics
Special Issues, Collections and Topics in MDPI journals
Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
Interests: computer systems design; computer architectures; operating systems; real-time systems; computer-based control; robotics; mechatronics, modelling and simulation
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, Sapienza University of Rome, 00185 Rome, Italy
Interests: computer vision (feature extraction and pattern analysis); scene and event understanding (by people and/or vehicles and/or objects); human–computer interaction (pose estimation and gesture recognition by hands and/or body); sketch-based interaction (handwriting and freehand drawing); human–behaviour recognition (actions, emotions, feelings, affects, and moods by hands, body, facial expressions, and voice); biometric analysis (person re-identification by body visual features and/or gait and/or posture/pose); artificial intelligence (machine/deep learning); medical image analysis (MRI, ultrasound, X-rays, PET, and CT); multimodal fusion models; brain–computer interfaces (interaction and security systems); signal processing; visual cryptography (by RGB images); smart environments and natural interaction (with and without virtual/augmented reality); robotics (monitoring and surveillance systems with PTZ cameras, UAVs, AUVs, rovers, and humanoids)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of information technology, intelligent devices have been widely used in people's daily lives. Among the many applications, intelligent transportation that relies on the integration of transportation infrastructure and vehicular technology has recently received a great deal of attention. A smart city based on the Internet, Internet of Things, telecommunications, radio and television, and wireless broadband network is a new model for modern city development, aiming to promote intelligent technology integration and features of efficient and convenient social services. The development of intelligent transportation systems (ITSs) is of great significance in the process of smart city development, seeking to effectively improve the efficiency of transportation systems, reduce energy consumption, lower transportation costs, and decrease impacts on the environment. Furthermore, intelligent traffic control plays a vital role in dealing with the issues of traffic congestion and accidents.

Current technologies applied to ITSs mainly include information technology, computer technology, data communication technology, sensor technology, electronic control technology, and artificial intelligence that can not only solve the problems in present transportation systems, but also be applied online with informatization and intelligent equipment to meet the development requirements of smart cities. Although the development of ITSs has received extensive attention and made significant breakthroughs, weaknesses such as an insufficient application of technologies, low efficiency of management, and poor service levels still remain.

The development of key ITS technologies is an indispensable part of realizing effective ITS applications for the development of smart cities. The purpose of this Special Issue is therefore to promote research concerning important aspects of ITS development in smart cities, focusing on the key enabling technologies.

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

  • AI technologies for intelligent transportation systems
  • Traffic modeling, simulation, prediction, and optimal control
  • Environment perception and recognition
  • Machine learning applications in autonomous driving
  • Deep reinforcement learning for autonomous vehicles
  • Path tracking and motion control vehicles
  • Internet of vehicles and smart transportation

Prof. Dr. Jingsha He
Prof. Dr. George K. Adam
Dr. Danilo Avola
Guest Editors

Manuscript Submission Information

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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.

Published Papers (6 papers)

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Research

18 pages, 3191 KiB  
Article
Gray Correlation Entropy-Based Influential Nodes Identification and Destruction Resistance of Rail-Water Intermodal Coal Transportation Network
Appl. Sci. 2024, 14(1), 77; https://doi.org/10.3390/app14010077 - 21 Dec 2023
Viewed by 481
Abstract
Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of [...] Read more.
Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China’s coal transportation model. To address this issue, this paper first establishes a static topological structure of China’s coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China’s coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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22 pages, 3203 KiB  
Article
Setting the Intermittent Bus Approach of Intersections: A Novel Lane Multiplexing-Based Method with an Intersection Signal Coordination Model
Appl. Sci. 2023, 13(18), 10098; https://doi.org/10.3390/app131810098 - 07 Sep 2023
Viewed by 655
Abstract
Intermittent bus lanes (IBLs) can alleviate the contradiction between bus priority and the urgent demand of general vehicles for road resources. However, existing IBL strategies seldom pay attention to the setting method of the dynamic bus lanes at intersections, which leads to the [...] Read more.
Intermittent bus lanes (IBLs) can alleviate the contradiction between bus priority and the urgent demand of general vehicles for road resources. However, existing IBL strategies seldom pay attention to the setting method of the dynamic bus lanes at intersections, which leads to the still serious delay of buses at intersections in the traffic congestion environment. To tackle this issue, this research explores a novel method of setting the intermittent bus approach (IBA) of intersections for lane sharing and bus priority at intersections. In particular, a time slice division strategy with an intersection signal coordination model is developed to fully and reasonably allocate the idle time of bus lanes at intersections. Besides, considering the lane-changing demands of general vehicles at intersections, the parameters of the IBA lane system are modeled and optimized. For testing and verifying the feasibility of the proposed method, comparative experiments are conducted through microscopic traffic simulation. Results show that the proposed IBA setting method can effectively solve the problem of bus priority failure at intersections. It can maintain the continuity of vehicle running on intersection sections, which better exerts the operational benefits of dynamic bus lanes. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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21 pages, 13656 KiB  
Article
An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
Appl. Sci. 2023, 13(15), 8759; https://doi.org/10.3390/app13158759 - 28 Jul 2023
Viewed by 753
Abstract
This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated [...] Read more.
This paper proposes an improved model based on YOLOv5s, specifically designed to overcome the challenges faced by current target detection algorithms in the field of electric bike helmet detection. In order to enhance the model’s ability to detect small targets and densely populated scenes, a specialized layer dedicated to small target detection and a novel loss function called Normalized Wasserstein Distance (NWD) are introduced. In order to solve the problem of increasing model parameters and complexity due to the inclusion of a small target detection layer, a Cross-Stage Partial Channel Mixing (CSPCM) on top of Convmix is designed. The collaborative fusion of CSPCM and the Deep Feature Consistency (DFC) attention mechanism makes it more suitable for hardware devices. In addition, the conventional Nearest Upsample technology is replaced with the advanced CARAFE Upsample module, further improving the accuracy of the model. Through rigorous experiments on carefully constructed datasets, the results show significant improvements in various evaluation indicators such as precision, recall, mAP.5, and mAP.95. Compared with the unmodified YOLOv5s algorithm, the proposed enhanced model achieves significant improvements of 1.1%, 8.4%, 5.2%, and 8.6% on these indicators, respectively, and these enhancements are accompanied by a reduction of 778,924 parameters. The experimental results on our constructed dataset demonstrate the superiority of the improved model and elucidate its potential applications. Furthermore, promising improvements for future research are suggested. This study introduces an efficient approach for improving the detection of electric bike helmets and verifies the effectiveness and practicality of the model through experiments. Importantly, the proposed scheme has implications for other target detection algorithms, especially in the field of small target detection. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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17 pages, 5566 KiB  
Article
Modeling and Control of Network Macroscopic Fundamental Diagram during Holidays: A Case Study of Qingming Festival in Tianjin
Appl. Sci. 2023, 13(14), 8399; https://doi.org/10.3390/app13148399 - 20 Jul 2023
Viewed by 517
Abstract
In this paper, the macroscopic traffic states and network traffic dynamics during the Qingming Festival holiday are explored with the Macroscopic Fundament Diagram (MFD), including the weekday before the holiday (WBH), the day of Qingming Festival (DQF), and the ordinary weekday (OW). The [...] Read more.
In this paper, the macroscopic traffic states and network traffic dynamics during the Qingming Festival holiday are explored with the Macroscopic Fundament Diagram (MFD), including the weekday before the holiday (WBH), the day of Qingming Festival (DQF), and the ordinary weekday (OW). The network is heterogeneous on the densities’ distribution, and the congested areas are different in location and size. Normalized Cut (Ncut) algorithm is used to partition the heterogeneous network into multiple homogeneous subregions. The MFD of each subregion is distributed within the critical density on the WBH, and the high-density region moves from the city center to the periphery. On the DQF, high-density areas are on the central and west of the urban network. The congested branch appears on the MFD of the subregion. Then, two calibrated dynamic models, are applied to analyze the network evolution based on the partitioning results. On the basis of the calibrated model, several control strategies are proposed to relieve regional congestion on holidays. According to the simulation results, congestion on the DQF can be alleviated by controlling the external inflow ratio of subregion 2 or limiting the amount of traffic entering subregion 2 from the outside. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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18 pages, 3297 KiB  
Article
Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes
Appl. Sci. 2023, 13(14), 8335; https://doi.org/10.3390/app13148335 - 19 Jul 2023
Viewed by 582
Abstract
A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the [...] Read more.
A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the time series pressure data into model elements in the model space, ensuring simplicity and stability. Various fitting functions, including Fourier basis, Gaussian basis, polynomial basis, sine basis, and others, are employed to accurately fit the pressure curve of the air brake pipe. The fault diagnosis process involves two steps: classifying the fault based on an optimal approximation equation and diagnosing it by analyzing the topological relationship of the model elements in the model space. The proposed method achieves an average fault diagnosis accuracy of 89.8%, with high accuracy rates for different fault states: 98% for normal state, 88% for blockage state, 84% for leakage state, and 96% for compressor fault state. Compared to the hidden Markov model method, the proposed method improves the average diagnostic accuracy by 2% for known working conditions and 4.87% for all working conditions, demonstrating its effectiveness and reliability. The fault diagnosis of the air brake tube in high-speed trains is of great significance, which aims to realize accurate fault diagnosis and prediction through sensor data monitoring and signal processing technology, so as to ensure the safe operation of high-speed trains. These studies provide an important theoretical and practical basis for the improvement and application of fault diagnosis methods. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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20 pages, 13476 KiB  
Article
Research and Application of the Obstacle Avoidance System for High-Speed Railway Tunnel Lining Inspection Train Based on Integrated 3D LiDAR and 2D Camera Machine Vision Technology
Appl. Sci. 2023, 13(13), 7689; https://doi.org/10.3390/app13137689 - 29 Jun 2023
Viewed by 1079
Abstract
This study presents an innovative, intelligent obstacle avoidance module intended to significantly enhance the collision prevention capabilities of the robotic arm mechanism onboard a high-speed rail tunnel lining inspection train. The proposed module employs a fusion of ORB-SLAM3 and Normal Distribution Transform (NDT) [...] Read more.
This study presents an innovative, intelligent obstacle avoidance module intended to significantly enhance the collision prevention capabilities of the robotic arm mechanism onboard a high-speed rail tunnel lining inspection train. The proposed module employs a fusion of ORB-SLAM3 and Normal Distribution Transform (NDT) point cloud registration techniques to achieve real-time point cloud densification, ensuring reliable detection of small-volume targets. By leveraging spatial filtering, cluster computation, and feature extraction, precise obstacle localization information is further obtained. A fusion of multi-modal data is achieved by jointly calibrating 3D LiDAR and camera images. Upon validation through field testing, it is demonstrated that the module can effectively detect obstacles with a minimum diameter of 0.5 cm, with an average deviation controlled within a 1–2 cm range and a safety margin of 3 cm, effectively preventing collisions. Compared to traditional obstacle avoidance sensors, this module provides information across more dimensions, offering robust support for the construction of powerful automated tunnel inspection control systems and digital twin lifecycle analysis techniques for railway tunnels. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems in Smart Cities)
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