Development Of Intelligent Transportation Systems Research Articles

Privacy-Preserving Distributed Transfer Learning and its Application in Intelligent Transportation

With the rapid development of intelligent transportation systems (ITS), more and more intelligent applications for ITS have received widespread attention, such as the vehicle detection, inference of typical routes, and traffic forecasting. In these applications, deep learning is widely used as a key artificial intelligence technology. However, most ITS providers fail to collect enough labeled traffic data for model training. As a complement to deep learning, transfer learning is an effective way to solve the scarcity of labeled data, which can transfer knowledge from labeled datasets to unlabeled datasets, thus improving the accuracy of prediction and classification. Nevertheless, when the labeled dataset and the unlabeled dataset are held by different entities, it is still unrealistic for two mutually distrustful entities to cooperate in transfer learning regarding data security and privacy preservation. Although some existing works provide privacy-preserving transfer learning methods, such methods fail to apply to traffic data with high sample dimensions due to their high computational cost and round complexity. To address this problem, we design an efficient privacy-preserving distributed transfer learning protocol, which is appropriate for traffic data. Compared to existing works, our protocol addresses the privacy-preserving problem of transfer learning for traffic data with high sample dimensions. In addition, our protocol has fewer interaction rounds and can be proved in the semi-honest model. Finally, we validate the effectiveness, efficiency and security of the proposed protocol via experiments. Furthermore, we show the application of the proposed protocol in intelligent transportation systems.

Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects.

Multi-level Graph Memory Network Cluster Convolutional Recurrent Network for traffic forecasting

Traffic forecasting plays a vital role in the management of urban road networks and the development of intelligent transportation systems. To effectively capture spatial and temporal dependencies within traffic data, Graph Neural Networks (GNNs) emerge by combining recurrent neural networks and dynamic graph learning. However, traditional GNNs face challenges in efficiently memorizing patterns at different levels of these dependencies, including long-term shared patterns and short-term specific patterns. We propose a traffic forecasting model, the Multi-level Graph Memory Network Cluster Convolutional Recurrent Network (MMNCCRN). The MMNCCRN consists of four modules: Encoder Module (EM), Attention Module (AM), Memory Network Cluster Module (MNCM), and Decoder Module (DM). To enhance the model’s performance, AM incorporates a concise yet efficient attention mechanism that augments important information in the output of EM. This alleviates the pressure on MNCM to identify patterns at different levels. Meanwhile, in MNCM, we extensively leverage memory networks and introduce the concept of clustering. By storing and memorizing patterns implicit in spatial and temporal dependencies, MNCM assists the model in learning underlying graph structures and discovering new hidden patterns. Experimental results show that our model outperforms others in most metrics on four datasets.

Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN

Traffic prediction is a pivotal technology in the development of intelligent transportation systems. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model. Initially, shared bicycle travel data is scrutinized for spatiotemporal characteristics. Subsequently, an adjacency matrix is constructed based on the spatial attributes of the data to establish the graph convolutional network. Lastly, global spatial autocorrelation analysis and a coupled coordination model are integrated for validation and augmentation. This model is employed to predict shared bicycle travel patterns near public transportation hubs in the Xiamen Island area. Experimental results demonstrate a high level of precision, underscoring the model's effectiveness in providing valuable guidance for shared bicycle travel. The effectiveness of this method can be further verified in subsequent empirical studies.

Intelligent transport systems in Russian megacities: the essence, structure and directions of development

Aim: to develop directions and determine the stages of development of intelligent transport systems in Russian agglomerations, taking into account their essential features, the effects they generate and the existing problems.
 Materials and Methods: in order to achieve the research goal, at the first stage, systematization and comparative analysis of scientific results obtained by Russian and foreign scientists on the chosen topic are used. At the second stage, the authors use the methods of summarizing and grouping the results obtained, statistical analysis. At the third stage, the generalization method is used to formulate recommendations for the development of intelligent transport systems. The information base of the study was materials from open sources, periodicals, as well as Internet resources, including analytical reports and materials from the Federal State Statistics Service.
 Results: The main scientific result is to determine the directions and stages of development of intelligent transport systems in Russian agglomerations based on the tasks they solve in the field of urban passenger transport management. In addition, a list of effects generated by the development of intelligent transport systems has been defined. The problems hindering the development of intelligent transport systems in Russian agglomerations have been identified.
 Conclusion: as a result of the research, the authors have formed the stages of development of intelligent transport systems, suggesting their integration with the concept of "Mobility as a service" in Russian agglomerations. The results can be used by both passenger transport companies and state executive authorities in the regions of the Russian Federation.

Microelectronics innovation and implementation in intelligent transportation systems

Under the background of urbanization and rapid development of transportation, the innovation of intelligent transportation system has become the key to improving traffic efficiency, relieving traffic pressure, and solving traffic problems. With the continuous progress of microelectronics technology, its application in the field of intelligent transportation is becoming more and more eye-catching. This paper focuses on the innovation and realization of micro-electronic technology in intelligent transportation system, and discusses the application of micro-electronic technology in intelligent navigation, intelligent parking, traffic flow optimization, etc. Through a literature review approach, this study demonstrates how microelectronics technology can drive the development of intelligent transportation systems to improve the efficiency and sustainability of urban transportation. The research results show that microelectronics technology not only brings revolutionary changes to the field of intelligent transportation, but also provides accurate positioning and navigation functions in intelligent navigation systems, realizes more efficient parking process management in intelligent parking systems, and plays a key role in traffic flow optimization. Microelectronic technology has wide application prospects and a positive social influence in the field of intelligent transportation.

A Review of Deep Learning-Based Vehicle Motion Prediction for Autonomous Driving

Autonomous driving vehicles can effectively improve traffic conditions and promote the development of intelligent transportation systems. An autonomous vehicle can be divided into four parts: environment perception, motion prediction, motion planning, and motion control, among which the motion prediction module plays an essential role in the sustainability of autonomous driving vehicles. Vehicle motion prediction improves autonomous vehicles’ understanding of the surrounding dynamic environment, which reduces the uncertainty in the decision-making system and facilitates the implementation of an active braking system for autonomous vehicles. Currently, deep learning-based methods have become prevalent in this field as they can efficiently process complex scene information and achieve long-term prediction. These methods often follow a similar paradigm: encoding scene input to obtain the context feature, then decoding the context feature to output predictions. Recent research has proposed innovative improvement designs to enhance the primary paradigm. Thus, we review recent works based on their improvement designs and summarize them based on three criteria: scene input representation, context refinement, and prediction rationality improvement. Although most works focus on trajectory prediction, this paper also discusses new occupancy flow prediction methods. Additionally, this paper outlines commonly used datasets, evaluation metrics, and potential research directions.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia

The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuation.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Tensor-Empowered LSTM for Communication-Efficient and Privacy-Enhanced Cognitive Federated Learning in Intelligent Transportation Systems

Multimedia cognitive computing as a revolutionary emerging concept of artificial intelligence emulating the reasoning process like human brains can facilitate the evolution of intelligent transportation systems (ITS) to be smarter, safer, and more efficient. Massive multimedia traffic big data is an important prerequisite for the success of cognitive computing in ITS. However, traditional data-centralized artificial intelligence approaches often face the problems of data islands and data famine due to concerns about data privacy and security. To this end, we propose the concept of cognitive federated learning leveraging federated learning as the learning paradigm for cognitive computing, which solves the preceding concerns by sharing updated models rather than raw data. Nevertheless, the exchange of numerous model parameters not only generates significant communication overhead but also suffers from the risk of privacy leakage due to inference attacks. This article aims to design a novel lightweight and privacy-enhanced cognitive federated learning architecture to facilitate the development of ITS. First, a privacy-enhanced model protection scheme with homomorphic encryption as the underlying technology is proposed to simultaneously defend against the inference attacks launched by external malicious attackers, honest-but-curious cognitive platforms, and internal participants. Furthermore, a novel tensor ring-block decomposition and its corresponding deep computation model converting the weight tensor into a set of matrices and third-order core tensors are proposed, which could reduce the communication overhead and storage requirements without compromising model performance. Experimental results on real-world datasets show that the proposed approach performs well.

Potential field-based modeling and stability analysis of heterogeneous traffic flow

Car-following behavior is influenced by multiple factors. Utilizing the potential field theory, previously determined as a comprehensive representation of these influencing factors, this study introduces a unified potential field-based car-following model tailored for mixed traffic flow. Considering the diverse information receptivity of various vehicle types, the model is shaped to encapsulate both Human Driven Vehicles and Connected and Automated Vehicles. Using the characteristic equation-based method, the stability conditions of the model are delineated. Subsequently, a detailed examination of mixed traffic flow stability was conducted across varying Connected and Automated Vehicles penetration rates. Leveraging a dataset of 300 real-world trajectories, the results were benchmarked against classical models such as the Intelligent Driver Model. Notably, our proposed model exhibited an 18.67 % improvement in safety metrics and a 1.36 % improvement in fuel economy over the Intelligent Driver Model when representing Connected and Automated Vehicles. The stability of the mixed traffic flow was found to correlate strongly with vehicular time delay and acceleration parameter weights. Furthermore, augmenting the Connected and Automated Vehicles penetration rate surfaced as a promising strategy to bolster traffic flow stability. Collectively, these findings furnish substantial enhancements to the existing body of knowledge on mixed traffic flow stability, offering pragmatic insights for future research and real-world applications, including traffic management, urban planning, and the development of Intelligent Transport Systems.

REGULATORY AND LEGAL ENSURING THE SUSTAINABLE DEVELOPMENT OF TRANSPORT AND LOGISTICS SYSTEMS

Topicality. The scientific article is devoted to the research analysis of the peculiarities of legal regulation and the construction of the architecture of the intellectualization of transport systems. Transport communications unite companies around the world and are a necessary prerequisite for the integrity of the country and its integration into the world economy through foreign economic activity. In this regard, the transport system is directly related to the economy and the social sphere. The transport policy of any state is characterized by national priorities at the micro and macro levels. A competitive transport system must have the following characteristics: be economically, ecologically and socially sustainable; to be able to recover effectively after natural disasters; have a developed and reliable IT infrastructure.The use of information systems in traffic management is associated with the rapid development of modern cities, an increase in the number of rolling stocks on the streets, new routes and other features. As a result, the amount of information needed for analysis and making operational decisions has increased significantly. The information component will become an integral part of city life. And for more effective use of all the possibilities of intelligent transport systems (ITS), legal regulation is necessary, which includes data protection, standardization, rules for the application of ITS achievements and their further development.Aim and tasks. The purpose of the research is to study the legal foundations of the concept of transport and logistics systems sustainable development.Materials and Methods. The research methodology is based on the analysis of legislative and legal methods of ensuring transport and logistics flows, economic and legal analysis using the general legal framework and special legal acts that regulate the activity of transport and logistics systems. The problem of intellectualization of environmental protection activities is considered both through structural changes in the international economy, through modern processes of development of national transport and logistics systems, as well as through the study of the complex network structure of global logistics chains, where both approaches complement each other.Research results. The rapid development of modern cities, the increase of vehicles on the streets, the development of new routes increases the amount of information necessary for analysis and making operational decisions. This, in turn, stimulates the active use of intelligent information technologies. And the creation of legal incentives for the development of the intellectualization of the transport and logistics system is one of the factors of strengthening the economy and improving the welfare of the population.Conclusion. The conducted research made it possible to conduct an analysis of legal regulation and stimulation of the development of intelligent transport systems (ITS). In particular, at the international and regional levels. The main necessary elements of the comprehensive application of intelligent transport systems are standardization at the international level, in order to be able to create a global system for more efficient use of transport and achieving global goals in the safety of movement and reducing the impact of transport on the environment.

How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

Edge Intelligence in Intelligent Transportation Systems: A Survey

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network and better use edge big data. This paper surveys Edge Intelligence in Intelligent Transportation Systems. We first introduce the challenges ITS faces and explain the motivation of using EI in ITS. We then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and Edge Computing technologies used in EI-based ITS, are reviewed intensively. We discuss the edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, we discuss some of the challenges and future directions of using EI in ITS.

Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information.

Trend analysis of traffic management based on literature data mining and graph analysis tools

AbstractStudites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies.

A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.

TGAE: Temporal Graph Autoencoder for Travel Forecasting

With the development of intelligent transportation systems, timely and accurate travel forecasting task has witnessed growing interest. Unlike most previous research that only considers the demand prediction in origin regions, this task aims to predict the origin-destination demand between all-region pairs. Its main challenges come from effectively capturing the direction, weight, and temporal information of links in dynamic traffic networks. To confront these challenges, we treat the dynamic traffic networks as multiple weighted directed network snapshots and propose a graph-based deep learning framework, Temporal Graph Autoencoder (TGAE). Specifically, TGAE encodes the fundamentally asymmetric nature of a directed graph via directed neighborhood aggregation and learns a pair of vector representations for each node. Meanwhile, we use the graph attention mechanism to capture the weight information of links. Next, TGAE preserves the temporal dependencies by independently reconstructing the existence and weight of links over two consecutive time steps. Furthermore, we employ the Long Short-Term Memory network (LSTM) to capture the evolution patterns of traffic networks and predict both the direction and weight of links based on the historical data. Experimental results demonstrate that TGAE outperforms several baseline methods on the travel forecasting task, which can help traffic management, resource preallocation, and services optimization.

Studies Advanced in License Plate Recognition

License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather conditions that can affect recognition accuracy. Ultimately, it is hoped that these insights will contribute to the development of intelligent transportation systems.