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Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5424

Special Issue Editor

Dr. Yongxing Li

E-Mail Website
Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Interests: transportation engineering; intelligent transportation systems; pedestrian flow

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) combine advanced information and telecommunication technologies and various data sources for improving efficiency and safety of transportation. ITS is an integrated system that implements a broad range of communication, control, vehicle sensing, and electronics technologies. These systems gather data from sensors and equipment deployed within vehicles and infrastructure and provide services that aim to help in monitoring and managing traffic flow, reducing congestion, enhancing productivity of the system, and making it more efficient, sustainable, safe, and environmentally friendly.

Topics of interest include, but are not limited to, the following topics:

  • New paradigms for ITS/vehicular communication;
  • Novel Architectures for ITS;
  • Data distribution platforms for ITS;
  • Medium access for vehicular communication;
  • Real-time sensing for autonomous vehicles;
  • Automatic incident detection and recovery;
  • Safety aspects of smart mobility;
  • Advance parking and monitoring systems;
  • Real-time and dynamic prediction of traffic flows;
  • Urban traffic forecasting;
  • Public transport optimization;
  • Intelligent routing and traffic organization;
  • Organization of vehicular adhoc networks;
  • Connected and automated vehicle;
  • Cooperative driving automation;
  • Advanced transit systems;
  • Advanced driver assistance systems.

Dr. Yongxing Li
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. 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
  • IoTs
  • connected vehicle
  • vehicle sensing

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Published Papers (7 papers)

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Research

20 pages, 2340 KiB  
Article
Modeling and Analysis of Mixed Traffic Flow Considering Driver Stochasticity and CAV Connectivity Uncertainty
by Qi Zeng, Siyuan Hao, Nale Zhao and Ruiche Liu
Sensors 2025, 25(9), 2806; https://doi.org/10.3390/s25092806 - 29 Apr 2025
Abstract
As connected and autonomous vehicle (CAV) technologies are rapidly integrated into modern transportation systems, understanding the dynamics of mixed traffic flow involving both human-driven vehicles (HVs) and CAVs is becoming increasingly important, particularly under uncertain conditions. In this paper, we propose a car-following [...] Read more.
As connected and autonomous vehicle (CAV) technologies are rapidly integrated into modern transportation systems, understanding the dynamics of mixed traffic flow involving both human-driven vehicles (HVs) and CAVs is becoming increasingly important, particularly under uncertain conditions. In this paper, we propose a car-following model framework to investigate the combined effects of driver stochasticity and connectivity uncertainties of CAVs on mixed traffic flow. The proposed framework can capture the inherent stochastic variations in human driving behavior by extending the classic intelligent driver model (IDM) with a Langevin-type stochastic differential equation. A car-following model with multi-anticipation control is developed for CAVs, explicitly incorporating sensor noise, communication delays, and dynamic connectivity. Extensive numerical simulations demonstrate that higher CAV penetration leads to more stable traffic flows. Even with certain levels of connectivity uncertainty, CAVs can still effectively stabilize the traffic. However, driver stochasticity has a pronounced impact on traffic stability—greater variability in driver behavior tends to reduce overall stability. Furthermore, sensitivity analyses reveal that in pure CAV environments, sensor noise, communication delays and communication ranges can affect traffic stability and energy consumption. In contrast, in mixed traffic conditions, the inherent instability of HV behavior tends to dominate and diminish the relative influence of CAV connectivity-related uncertainties. These findings underscore the necessity of robust sensor fusion and error compensation strategies to fully realize the potential of CAV technology. In mixed traffic environments, measures should be taken to minimize the adverse effects of HVs on CAV performance. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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31 pages, 1214 KiB  
Article
Intra-Technology Enhancements for Multi-Service Multi-Priority Short-Range V2X Communication
by Ihtisham Khalid, Vasilis Maglogiannis, Dries Naudts, Adnan Shahid and Ingrid Moerman
Sensors 2025, 25(8), 2564; https://doi.org/10.3390/s25082564 - 18 Apr 2025
Viewed by 117
Abstract
Cooperative Intelligent Transportation Systems (C-ITSs) are emerging as transformative technologies, paving the way for safe and fully automated driving solutions. As the demand for autonomous vehicles accelerates, the development of advanced Radio Access Technologies capable of delivering reliable, low-latency vehicular communications has become [...] Read more.
Cooperative Intelligent Transportation Systems (C-ITSs) are emerging as transformative technologies, paving the way for safe and fully automated driving solutions. As the demand for autonomous vehicles accelerates, the development of advanced Radio Access Technologies capable of delivering reliable, low-latency vehicular communications has become paramount. Standardized approaches for Vehicular-to-Everything (V2X) communication often fall short in addressing the dynamic and diverse requirements of multi-service, multi-priority systems. Conventional vehicular networks employ static parameters such as Access Category (AC) in IEEE 802.11p-based ITS-G5 and Resource Reservation Interval (RRI) in C-V2X PC5 for prioritizing different V2X services. This static parameter assignment performs unsatisfactorily in dynamic and diverse requirements. To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. These adaptations are integrated with a priority-aware rate-control mechanism to enhance congestion management. Through extensive simulations conducted using NS3, our proposed strategies demonstrate superior performance compared to standardized methods, achieving improvements in one-way end-to-end latency, Packet Reception Ratio (PRR) and overall communication reliability. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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18 pages, 3933 KiB  
Article
Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System
by Nu Wen, Ying Zhou, Yang Wang, Ye Zheng, Yong Fan, Yang Liu, Yankun Wang and Minmin Li
Sensors 2025, 25(7), 2089; https://doi.org/10.3390/s25072089 - 26 Mar 2025
Viewed by 344
Abstract
In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems [...] Read more.
In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. It aims to solve resource allocation optimization and improve operational efficiency in edge computing environments. By doing so, it meets the real-time computing requirements of intelligent transportation business applications. By adopting node and sensor management mechanisms as well as efficient communication protocols, dynamic sensor-based data management of AI algorithm models was achieved, such as pedestrian object recognition, vehicle object detection, and ship object positioning. Experimental results show that while maintaining the same recall rate, the inference time is reduced to one tenth or even one twentieth of the original time. And this strategy can enhance privacy protection of sensor-based data. In the future research, we may consider integrating distributed computing under high load conditions to further optimize the response time of model loading and unloading for multi-service interaction, and enhance the balance and scalability of the system. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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19 pages, 2909 KiB  
Article
The Path Planning Problem of Robotic Delivery in Multi-Floor Hotel Environments
by Linghui Han, Junzhe Ding, Songtao Liu and Meng Meng
Sensors 2025, 25(6), 1783; https://doi.org/10.3390/s25061783 - 13 Mar 2025
Viewed by 400
Abstract
Robots have been widely adopted in transportation and delivery applications. Path planning plays a critical role in determining the performance of robotic systems in these tasks. While existing research has predominantly focused on path planning for single robots and the design of robot [...] Read more.
Robots have been widely adopted in transportation and delivery applications. Path planning plays a critical role in determining the performance of robotic systems in these tasks. While existing research has predominantly focused on path planning for single robots and the design of robot delivery systems based on hotel-specific demand characteristics, there is limited exploration of multi-robot collaborative routing in three-dimensional environments. This paper addresses this gap by investigating the multi-robot collaborative path planning problem in three-dimensional, multi-floor hotel environments. Elevator nodes are modeled as implicit waypoints, and the routing problem is formulated as a Multi-Trip Vehicle Routing Problem (MTVRP). To solve this NP-hard problem, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed. The effectiveness of the algorithm is validated through comparative experiments with Gurobi, demonstrating its ability to handle complex three-dimensional delivery scenarios. Numerical results reveal that the number of robots and elevator operation times significantly impact overall delivery efficiency. Additionally, the study identifies an imbalance in resource utilization, where certain robots are overused, potentially reducing their lifespan and affecting system stability. This research highlights the importance of efficient multi-robot routing in three-dimensional spaces and provides insights into optimizing delivery systems in complex environments. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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13 pages, 3131 KiB  
Article
Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
by Lukai Zhang, Xiaoya Wang and Yingping Wang
Sensors 2024, 24(24), 7930; https://doi.org/10.3390/s24247930 - 11 Dec 2024
Viewed by 758
Abstract
Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in [...] Read more.
Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks. Initially, using the Highway Asphalt Pavement Design Specification, it analyzes the demand for these calculations in road sections. Considering the current axle load detection coverage, a method supported by highway traffic data is proposed. For integrating multi-source data, a generalized regression neural network model is established, enabling deep learning calculations. The method is validated and applied to Xuzhou’s highway network. Results show consistency between the calculated average axle load spectrum and actual data. Among validation samples, 3-axle vehicles exhibit the smallest deviation, while 6-axle vehicles show the largest. Calculating equivalent axle numbers reveals the distribution and grading of heavily loaded road sections, aiding maintenance decisions. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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12 pages, 1635 KiB  
Article
Investigating Influence Factors on Traffic Safety Based on a Hybrid Approach: Taking Pedestrians as an Example
by Yue Li, Yuanyuan Shi, Huiyuan Xiong, Feng Jian, Xinxin Yu, Shuo Sun and Yunlong Meng
Sensors 2024, 24(23), 7720; https://doi.org/10.3390/s24237720 - 3 Dec 2024
Viewed by 829
Abstract
Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation [...] Read more.
Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian–vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors “Workday or not” (Not), “Signal control mode” (No signal and Other security facilities), and “Road safety attribute” (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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31 pages, 38217 KiB  
Article
A Three-Stage Cellular Automata Model of Complex Large Roundabout Traffic Flow, with a Flow-Efficiency- and Safety-Enhancing Strategy
by Xiao Liang, Chuan-Zhi Thomas Xie, Hui-Fang Song, Yong-Jie Guo and Jian-Xin Peng
Sensors 2024, 24(23), 7672; https://doi.org/10.3390/s24237672 - 30 Nov 2024
Viewed by 1502
Abstract
Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, [...] Read more.
Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate (ρ) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies)
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