Field Of Intelligent Transportation Systems Research Articles

Contrastive optimized graph convolution network for traffic forecasting

Traffic forecasting is an increasingly important research topic in the field of Intelligent Transportation Systems (ITS). In this field, prediction models based on Graph Convolution Networks (GCN) have become very popular. Most GCN-based models focus on constructing various optimized or dynamic road network graphs to represent the spatio-temporal correlation hidden in traffic data. However, these methods currently only consider the construction of a single improved road network graph and ignore the relationship of these existing optimized road network graphs. Therefore, in this paper, we propose a Contrastive Optimized Graph Convolution Network (COGCN) to connect two kinds of optimized road network graphs and maintain their global–local feature consistency through contrastive learning. The proposed COGCN model is evaluated in detail using four real traffic datasets: two traffic speed datasets and two traffic flow datasets. Experimental results show that COGCN improves forecasting accuracy by at least 2% on the two speed datasets and 9% on the two flow datasets compared to the existing state-of-the-art GCN-based methods.

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.

Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques

In the evolving field of Intelligent Transportation Systems (ITSs), accurate and reliable traffic prediction is essential in enhancing management and planning capabilities. Accurately predicting traffic conditions over both short-term and long-term intervals is vital for the practical application of ITS. The integration of deep learning into traffic prediction has proven crucial in advancing traffic prediction beyond traditional approaches, particularly in analyzing and forecasting complex traffic scenarios. Despite these advancements, the existing methods are unable to effectively handle both short-term and long-term traffic patterns given their complex nature, revealing a need for more comprehensive forecasting solutions. To address this need, we propose a new approach named the Short-Term and Long-Term Integrated Transformer (SLIT). SLIT is a Transformer-based encoder–decoder architecture, designed for the effective prediction of both short-term and long-term travel time durations. The architecture integrates the Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder–Decoder (SLIED). This harmonious combination enables SLIT to effectively capture the complexities of traffic data over varying time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the excellence of SLIT compared with existing competitive methods in both short- and long-term travel time predictions across various metrics. SLIT exhibits significant improvements in prediction results, particularly in short-term forecasting. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67% in terms of all evaluation metrics across various time horizons. Furthermore, SLIT demonstrates the capability to analyze traffic patterns across various road complexities, proving its adaptability and effectiveness in diverse traffic scenarios with improvements of up to 10.83% in different road conditions. The results of this study highlight the high potential of SLIT in significantly enhancing traffic prediction within ITS.

Implementation of Smart Vehicle Assistant using Computer vision and Deep Learning

bstract: Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency. Scene classification is a key technology for autonomous cars because it forms the basis of these vehicles' decision-making. In recent years, deep learning-based methods have demonstrated promise in resolving the scene classification issue. However, further research is needed on a few difficulties related to scene classification methodologies, such as how to treat similarities and contrasts within the same category. To tackle these problems, this research suggests an improved deep network-based scene classification method. Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency.

Intelligent Transportation System Technologies, Challenges and Security

Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.

Multi-Source Information Fusion Graph Convolution Network for traffic flow prediction

As a fundamental technology in the field of intelligent transportation systems, traffic flow prediction has a wide range of applications. The utilization of Graph Convolutional Network (GCN) models is notable for capturing the complex spatial–temporal dependencies in traffic data, leading to a significant improvement in prediction accuracy. However, most existing graph construction methods overlook joint impact of auxiliary information such as weather and traffic speed on the road topology. Moreover, the research on interactions within time series at coarse temporal resolutions remains insufficiently explored, giving rise to unsatisfactory long-term prediction performance. In this study, we present a novel framework, namely Multi-Source Information Fusion Graph Convolution Network (MIFGCN), for spatial–temporal traffic flow prediction. Our key innovation lies in creating a dynamic graph that integrates weather, traffic speed, and global spatial information, effectively simulating significant traffic fluctuations caused by subtle ancillary information in the road network. Simultaneously, it captures the evolving hidden adjacency relationships between nodes over time. Furthermore, by combining with an attention-based temporal interaction module, MIFGCN learns multiscale temporal correlations at course temporal resolutions, enhancing the ability for long-term prediction. Experiments conducted on four real-world traffic datasets demonstrate that MIFGCN outperforms various state-of-the-art baselines, especially achieving a 10.50% average improvement on the PeMS08 dataset.

Detection of black hole attacks in vehicle-to-vehicle communications using ad hoc networks and on demand protocols

PurposeThe potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.Design/methodology/approachThe black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.FindingsThrough the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.Originality/valueAll things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Research on the Application of Artificial Intelligence Technology in the Field of Intelligent Transportation Systems

Research on the Application of Artificial Intelligence Technology in the Field of Intelligent Transportation Systems

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

The main features of the creation and operation of the service band of the ITS integration platform

This paper outlines the main structural features of the creation and development of the service lane of the integration platform of the intelligent transportation system (hereinafter referred to as ITS), as well as the main aspects of the functioning of the service lane of the ITS integration platform. The main purpose of this article is to conduct analysis in the context of integration and development of the service lane of the ITS integration platform in the transport complex of Russia In this paper the decomposition analysis of the components of the ITS service lane is carried out in the context of its application within the framework of the ITS integration platform, and the key existing approaches in the field of structuring intelligent transportation systems, which are the key indicators (criteria) of qualitative and effective development of all spheres of the economy, both at the level of the Russian Federation and the Eurasian Economic Union. This study is limited to the scientific field of intelligent transport systems, ITS standardization. The methodological basis of this study is analysis, inductive analysis, as well as synthesis.

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding.

Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.

A visual-based toolkit to support mobility data analytics

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results.To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching.To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience.Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data.

Adaptive traffic lights based on traffic flow prediction using machine learning models

<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>

Optimizing Traffic Control with Real-time Data: A HERE API-Based Approach

Traffic congestion is a pervasive issue in urban areas, leading to increased commute times, environmental pollution, and reduced overall quality of life. To address this challenge, this paper introduces a dynamic traffic control system that leverages the HERE API for real-time traffic data. The system provides a web- based interface with user authentication and allows users to input the address of a signal they wish to control. Utilizing the HERE API, it fetches live traffic information, including jam factor and free flow speed, for all four roads at the specified signal. This data serves as the foundation for an intelligent traffic management system that dynamically allocates green signal times based on the intensity of traffic, with roads experiencing higher congestion receiving longer green time. The system's backend is built using Python and the Flask framework, while the frontend features HTML and CSS for a user-friendly interface. Authentication ensures that only authorized users can access and utilize the system. Geocoding is employed to convert user-provided addresses into geographic coordinates, enabling precise data retrieval from the HERE API. Once the traffic data is collected, an analysis is performed to identify the road with the highest traffic intensity. The dynamic allocation of green signal times is a fundamental feature, allowing for real-time traffic optimization and a reduction in congestion. The paper explores the development, implementation, and testing of this dynamic traffic control system, emphasizing its potential to significantly improve urban mobility. Furthermore, considerations for scalability, security, and maintenance are addressed to ensure the system's practicality in real-world scenarios. This project contributes to the growing field of intelligent transportation systems by showcasing the benefits of data-driven decision-making in traffic signal control. By utilizing the HERE API, this system not only provides a solution to mitigate traffic congestion but also represents a step towards more sustainable and efficient urban transportation systems. Key Words: Traffic control, HERE API, Dynamic signal allocation, Real-time traffic data, Urban mobility, Traffic congestion, Traffic optimization, Intelligent transportation systems, Geocoding, User authentication, Python backend, Flask framework, User interface, Green signal time, Scalability, Security, Maintenance, Sustainable transportation.

Multisource fusion of exogenous inputs based NARXs neural network for vehicle speed prediction between urban road intersections

The economy and safety of passages through the urban road intersection environment is an important research topic in the field of intelligent transportation systems, but vehicle speed prediction as its subtopic is still under-researched, and its prediction accuracy is unsatisfactory. Therefore, a model for vehicle speed prediction based on the nonlinear autoregressive model with multisource exogenous inputs (NARXs) neural network is proposed. The model combines the human-vehicle-road model with the NARXs neural network to perform speed prediction between urban road intersections. First, multisource features, including the variables of driving behavior characteristics, vehicle responses, and road conditions, are extracted to construct the human-vehicle-road model. Then, the model is introduced into the NARXs neural network. Finally, the advantages of the proposed model are verified from two perspectives by evaluation indices such as mean absolute error ( MAE), mean absolute percentage error ( MAPE), root mean square error ( RMSE), Theil index ( Theil ic), and goodness-of-fit ( R2) compared with several other models. On the one hand, the analysis results show that the proposed model has higher prediction accuracy than the other comparative models for different prediction durations and has the best performance in 30 s duration backward prediction. On the other hand, the curves of each evaluation index of the proposed model are horizontal, which indicates that the prediction performance of the model hardly varies with the length of the training dataset. These positive results demonstrate the higher accuracy and outstanding characteristics of the proposed model in the subject of vehicle speed prediction.

A Tribute to Women in the Field of Intelligent Transportation Systems [President’s Message

A Tribute to Women in the Field of Intelligent Transportation Systems [President’s Message

Artificial Intelligence in Transportation Systems A Critical Review

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities.
 Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data.
 Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel.
 Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.

Valuable Resources and Opportunities for IEEE ITSS Members [President’s Message

The field of intelligent transportation systems (ITS) is rapidly growing and has had a significant impact on society as it aims to make transportation safer, more efficient, and more sustainable. The strong leadership of the IEEE ITS Society (ITSS), made up of a dedicated and experienced group of volunteers who are committed to advancing the field of ITS, is working to provide valuable resources and opportunities for members through its Chapters. The Society’s Chapters provide a local network of professionals and students, who are interested in the field of ITS. ITSS Chapters provide a place for members to connect with others in their area, share ideas, and stay current with the latest developments in their area of expertise.

Longitudinal Control-Oriented Spectrum Sharing Based on C-V2X for Vehicle Platoons

Combined with the development of wireless communication, the vehicle platoon will play a key role in the field of intelligent transportation system, due to its significant advantages in improving traffic safety, increasing road capacity, decreasing energy consumption, etc. In a vehicle platoon, the driving-related information is collected through vehicle-to-vehicle (V2V) communication to guarantee safety and improve control performance. However, it has been proved that the delay and reliability of V2V communication greatly affect the platoon stability. In this article, we investigate the spectrum-sharing problem for a vehicle platoon satisfying longitudinal control requirements. Particularly, we derive the communication delay constraints that guarantee plant stability and string stability. Then, using the obtained delay constraint, the wireless resource management, including the optimal power allocation and selection of spectrum sharing of the involved vehicles, is carried out in consideration of intracell interference and heterogeneous communication requirements. Finally, simulation results demonstrated the feasibility and effectiveness of the proposed scheme in vehicle-following control and wireless resource allocation.