Component Of Intelligent Transportation System Research Articles

Live Intersection Data Acquisition for Traffic Simulators (LIDATS)

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

Enhanced Traffic Light Guidance for Safe and Energy-Efficient Driving: A Study on Multiple Traffic Light Advisor (MTLA) and 5G Integration

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain.

Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction

As a critical component of Intelligent Transportation Systems (ITS), traffic flow prediction is indispensable for vehicle routing and transportation management. However, traffic prediction is an intractable task due to the complex spatial–temporal dependencies. First, traffic data has multiple spatial dependencies that arise from different contexts and vary with time. Second, multi-scale temporal features naturally exist in the traffic data and have dynamic significance for predicting future traffic states. Insufficiently capturing such spatial and temporal dependencies will lead to the prediction performance degradation. To this end, we propose a dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction. Unlike existing methods, we propose a novel attention layer assigning weights to each feature of nodes to augment the dynamic spatial dependence modeling and a dynamic multi-branch temporal network learning temporal features from the time domain and frequency domain complementary to enhance the dynamic multi-scale temporal features learning capability. Extensive experiments on four well-known traffic datasets demonstrate that the proposed model outperforms the best baselines with a ratio of 5.27%, 2.44%, 3.27%, and 7.34% in terms of mean absolute error and also has superior long-term prediction performance than state-of-the-art baselines.

Robust Long-Term Vehicle Trajectory Prediction Using Link Projection and a Situation-Aware Transformer.

The trajectory prediction of a vehicle emerges as a pivotal component in Intelligent Transportation Systems. On urban roads where external factors such as intersections and traffic control devices significantly affect driving patterns along with the driver's intrinsic habits, the prediction task becomes much more challenging. Furthermore, long-term forecasting of trajectories accumulates prediction errors, leading to substantially inaccurate predictions that may deviate from the actual road. As a solution to these challenges, we propose a long-term vehicle trajectory prediction method that is robust to error accumulation and prevents off-road predictions. In this study, the Transformer model is utilized to analyze and forecast vehicle trajectories. In addition, we propose an extra encoding network to precisely capture the effect of the external factors on the driving pattern by producing an abstract representation of the situation nearby the driver. To avoid off-road predictions, we propose a post-processing method, called link projection, which projects predictions onto the road geometry. Moreover, to overcome the limitations of Euclidean distance-based evaluation metrics in evaluating the accuracy of the entire trajectory, we propose a new metric called area-between-curves (ABC). It measures the similarity between two trajectories, and thus the accordance between the two can be effectively evaluated. Extensive evaluations are conducted using real-world datasets against widely-used methods to demonstrate the effectiveness of the proposed approach. The results show that the proposed approach outperforms the conventional deep learning models by up to 65.74% (RMSE), 60.13% (MAE) and 91.45% (ABC).

Robust H∞ control of heterogeneous connected vehicles platoon with random time-varying communication delay

As an essential component of intelligent transportation systems (ITS), connected cruise control (CCC) has attracted much attention due to its potential to improve vehicle safety and reduce traffic congestion. However, besides the disturbance acting on the head vehicle of the platoon, the time-varying communication delay in vehicle-to-vehicle (V2V) also seriously impacts the CCC system’s stability. In this paper, we investigate a robust control scheme utilizing the Lyapunov-Krasovskii functional stability theory and [Formula: see text] technique to ensure the stability of the CCC system consisting of the connected and automated vehicle (CAV) and connected human-driven vehicles (CHVs) under random time-varying communication delay. The effectiveness and advantage of the designed robust controller are verified by MATLAB/Simulink simulation and comparison with the existing result of the literature.

LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting

Traffic forecasting has always been a critical component of intelligent transportation systems. Due to the complexity of traffic prediction models, most research just only consider short-term historical data in the temporal dimension. However, learning temporal patterns necessitates the involvement of long-term historical data. Additionally, many models are limited in capturing spatial features by only considering short-distance spatial information connected to the target node. To solve these problems, we propose a dual-graph transformer, namely Long-term Correlations Dual-graph transFormer (LCDFormer), designed to capture long-term correlations and long-distance spatial correlations. It is entirely based on attention mechanisms, and as far as we know, there is limited research adopting this approach. Our work addresses this gap in the literature. In particular, we have devised a time aggregation method capable of consolidating long-term historical time series, concurrently addressing the impact of long-term temporal correlations while minimizing the influence of redundant data. Subsequently, we have introduced a novel spatio-temporal attention module that compresses spatial information to generate short-term input sequences while modeling dynamic long-range spatial correlations. We conducted extensive experiments with LCDFormer on five real-world traffic datasets. The results indicate that LCDFormer, considering long-term spatio-temporal correlations, is better able to learn the spatio-temporal patterns of traffic data. Compared to the current state-of-the-art baseline, our model has demonstrated outstanding predictive performance with a maximum improvement of 5.02% in mean absolute error, 4.33% in root mean square error and 7.32% in mean absolute percentage error. The source codes are available at: https://github.com/NanakiC/LCDFormer.

A fsQCA-Based Framework for Cybersecurity of Connected and Automated Vehicles: Implications for Sustainable Development Goals

Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks.

The Game Theory Application in the Transport Flows Control Algorithm Development in the Intelligent Transport System

Traffic management in modern intelligent transport systems includes monitoring the actual traffic situation in real time (volumes, speeds, incidents, etc.) and management or controlling transport traffic using this information. At the same time, cars as intelligent transport systems components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and with road infrastructure (V2I). All this is connected with the presence of special equipment connected to the on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of “smart” regulation of intersections is quite difficult to solve, it combines the efforts of scientists from different fields of knowledge. However, the rapid development of ICT technologies and their rapid application to transport tasks makes it possible to develop some approaches to optimize the current situation at the intersection.

Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

Algorithm for Control of Traffic Flow in an Intelligent Transport System

Traffic management in modern Intelligent Transport Systems (ITS) includes monitoring the actual traffic situation in real time and management transport traffic using this information. At the same time, cars as ITS components must be equipped with communication capabilities for exchanging information with other vehicles (V2V) and road infrastructure (V2I). Such approach is connected with usage of special equipment connected to on-board network for local data collection, which can be exchanged between cars and with a central communication station using wireless Internet. At the same time, the issue of developing the traffic organization algorithms themselves is still open. This problem can be solved with the help of game theory, a fairly new but rapidly developing part of modern mathematics. Unlike optimization theory, which studies the possibilities of constructing an optimal solution for the entire system as a whole, game theory studies ways to optimize individual benefits in competition with other persons (events) that rationally seek to satisfy their own benefits. The problem of "smart" regulation of intersections is quite difficult to solve a problem, nevertheless it is possible due to rapid development of ICT technologies. Keywords: ITS, transport crossroads, game theory

D4: Dynamic, Decentralized, Distributed, Delegation-Based Network Control and Its Applications to Autonomous Vehicles

Connected autonomous vehicles technology are expected to be an important component of Intelligent Transportation Systems (ITS). Several relevant industry standards are being created to prepare for the maturity of connected vehicles with the help of artificial intelligence, cognitive methods, software-hardware and sensor platforms. One of the significant challenges raised by connected vehicles is how the underlying communication infrastructure will be able to efficiently support them. In this paper, we propose the Dynamic, Decentralized, Distributed, Delegation-based (D4) network control plane architecture which aims at providing adequate communication support for connected vehicles. To our knowledge, D4 is the first framework to support on-demand network control decentralization. At its core, D4’s flexible network control delegation framework allows network control to be dynamically distributed on demand to address quality-of-service requirements of different connected vehicle services and applications. We demonstrate the benefits of D4 through a proof-of-concept prototype running on a fully reproducible emulation platform. Experimental results using a variety of real-world scenarios demonstrate that D4 delivers lower latency with minimal additional overhead. We also showcase the benefits of D4 when compared to traditional vehicular ad-hoc network (VANET) approaches.

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.

Combining jumping knowledge into traffic forecasting: An attention-based spatial-temporal adaptive integration gated network

Traffic forecasting has become a core component of Intelligent Transportation Systems. However, accurate traffic forecasting is very challenging, caused by the complex traffic road networks. Most existing forecasting methods do not fully consider the topological structure information of road networks, making it difficult to extract accurate spatial features. In addition, spatial and temporal features have different impacts on traffic conditions, but the existing studies ignore the distribution of spatial-temporal features in traffic regions. To address these limitations, we propose a novel graph neural network architecture named Attention-based Spatial-Temporal Adaptive Integration Gated Network (AST-AIGN). The originality of AST-AIGN is to obtain a spatial feature that more accurately reflects the topological structure of the road networks by embedding Graph Attention Network (GAT) into Jumping Knowledge Net (JK-Net). We propose a data-dependent function called spatial-temporal adaptive integration gate to process the diversity of feature distribution and highlight features in road networks that significantly affects traffic conditions. We evaluate our model on two real-world traffic datasets from the Caltrans Performance Measurement System (PEMS04 and PEMS08), and the extensive experimental results demonstrate the proposed AST-AIGN architecture outperforms other baselines.

STGAFormer: Spatial–temporal Gated Attention Transformer based Graph Neural Network for traffic flow forecasting

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS). However, the dynamic temporal variations in traffic flow, especially in potential occurrence of unexpected incidents, pose challenges to the prediction of traffic flow. This paper proposes a Spatial–temporal Gated Attention Transformer (STGAFormer) model based Graph Neural Network(GNN), leveraging the encoder architecture of the transformer. The gated temporal self-attention in the model, a novel module, can improve the model’s ability to make long-term predictions and handle sudden traffic incidents by enhancing the extraction of both local and global temporal features. Additionally, this paper proposes a distance spatial self-attention module to extract spatial features, which employs thresholding to selectively identify crucial features from both nearby and distant regions. In this way, the model’s ability to assimilate critical spatial information is promoted. Moreover, our model incorporates a diverse range of inputs, including traffic flow attributes, periodicity, proximity adjacency matrix, and adaptive adjacency matrix. Experiments from four real datasets demonstrate that STGAFormer achieves state-of-the-art performance, especially the MAE value of the PeMS08 dataset experiment is improved by 3.82%. This method offers valuable insights and robust support for future transportation planning.

Traffic flow prediction with a multi-dimensional feature input: A new method based on attention mechanisms

<abstract> <p>Accurately predicting traffic flow is an essential component of intelligent transportation systems. The advancements in traffic data collection technology have broadened the range of features that affect and represent traffic flow variations. However, solely inputting gathered features into the model without analysis might overlook valuable information, hindering the improvement of predictive performance. Furthermore, intricate dynamic relationships among various feature inputs could constrain the model's potential for further enhancement in predictive accuracy. Consequently, extracting pertinent features from datasets and modeling their mutual influence is critical in attaining heightened precision in traffic flow predictions. First, we perform effective feature extraction by considering the temporal dimension and inherent operating rules of traffic flow, culminating in Multivariate Time Series (MTS) data used as input for the model. Then, an attention mechanism is proposed based on the MTS input data. This mechanism assists the model in selecting pertinent time series for multivariate forecasting, mitigating inter-feature influence, and achieving accurate predictions through the concentration on crucial information. Finally, empirical findings from real highway datasets illustrate the enhancement of predictive accuracy attributed to the proposed features within the model. In contrast to conventional machine learning or attention-based deep learning models, the proposed attention mechanism in this study demonstrates superior accuracy and stability in MTS-based traffic flow prediction tasks.</p> </abstract>

Sustainable and Transferable Traffic Sign Recognition for Intelligent Transportation Systems

Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in popularity in recent years. However, since these models belong to the closed-world-oriented learning paradigm, they are only capable of accurately identifying traffic signs that are easy to collect and cannot adapt to the real world. Furthermore, the sample utilization of these methods is insufficient, the resource consumption of model training may become unbearable as the data scale grows. To address this problem, we propose a novel “knowledge <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> data” co-driven solution (i.e., Joint Semantic Representation algorithm, JSR) for TSR. JSR creates a hybrid feature representation by extracting general and principal visual features from traffic sign images. It also realizes the model’s reasoning ability to zero-shot TSR based on prior knowledge of traffic sign design standards. The effectiveness of JSR is demonstrated by experiments on four benchmark datasets and two self-built TSR datasets.

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems, ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving. Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles. The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety. Specifically, an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics. In addition, an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics. Moreover, we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model. Finally, the proposed approach is evaluated through both simulations and experiments. These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System

Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, the combination of road scene-graph representations and graph learning techniques has demonstrated superior performance compared to cutting-edge deep learning methods across various tasks such as action classification, risk assessment, and collision prediction. It's a grueling problem due to large variations under different illumination conditions. Transfer learning is a promising approach to address this challenge. Transfer learning involves leveraging pre-trained deep learning models on large-scale datasets to develop efficient models for new tasks with limited data. In the context of ITS, transfer learning can enable accurate scene understanding with less data by reusing learned features from other domains.&#x0D; This paper presents a comprehensive overview of the application of transfer learning for scene understanding in cross domain. It highlights the benefits of transfer learning for ITS and presents various transfer learning techniques used for scene understanding. This survey paper provides systematic review on cross domain outdoor scene understanding and transfer learning approaches from different perspective, presents information on current state of art and significant methods in choosing the right transfer learning model for specific scene understanding applications.

Simulating MIMT Attackers' Strategies in VANET to Secure ITS in Smart Cities via Multiverse Optimization-based Hybrid Routing Approach

The connection between moving vehicles and stationary Road Side Units is made possible by VANET technology, which is an essential component of Intelligent Transportation Systems. Vanet based intelligent transportation system (ITS) security is major issue in present days. MIMT Attackers' Strategies are taken into account to test the security of VANET based ITS system in present research paper. The major objective of research paper is to evaluate the potential of security model in case of different type of message dropping and delay attack. It is observed that there is probability of message delay, message drop, and message tampering attack in VANET based ITS system. Due to such attacks there is huge impact on content delivery ratio, packet delay and dropping. Thus proposed hybrid routing approach that is considering Multi-verse optimization has been used to simulate the Quantifying MIMT attacks. In present research, Vanet security in case of intelligent transportation system in smart cities has been considered.

A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system’s efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications’ inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle’s proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge Computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle’s computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the scheduling overhead, improves the utilization of system resources, and minimizes the data transmission delay caused by vehicle motion.