Development Of Intelligent Transportation Systems Research Articles

Data fusion for ITS: A systematic literature review

In recent years, the development of intelligent transportation systems (ITS) has involved the input of various kinds of heterogeneous data in real time and from multiple sources, which presents several additional challenges. Studies on Data Fusion (DF) have delivered significant enhancements in ITS and demonstrated a substantial impact on its evolution. This paper introduces a systematic literature review on recent data fusion methods and extracts the main issues and challenges of using these techniques in intelligent transportation systems (ITS). It endeavors to identify and discuss the multi-sensor data sources and properties used for various traffic domains, including autonomous vehicles, detection models, driving assistance, traffic prediction, Vehicular communication, Localization, and management systems. Moreover, it attempts to associate abstractions of observation-level fusion, feature-level fusion, and decision-level fusion with different methods to better understand how DF is used in ITS applications. Consequently, the main objective of this paper is to review DF methods used for ITS studies to extract its trendy challenges. The review outcomes are (i) a description of the current Data fusion methods that adopt multi-sensor sources of heterogeneous data under different evaluation strategies, (ii) identifying several research gaps, current challenges, and new research trends.

Development of a model for detecting network traffic anomalies in distributed wireless ad hoc networks

Mobile ad hoc networks are one of the promising directions of the edge computing technology and they are used in various applications, in particular, in the development of intelligent transport systems. A feature of mobile ad hoc networks lies in the constantly changing dynamic network topology, as a result of which it is necessary to use reactive routing protocols when transmitting packets between nodes. Mobile ad hoc networks are vulnerable to cyber-attacks, so there is a need to develop measures to identify network threats and develop rules for responding to them based on machine learning models. The subject of this study is the development of a dynamic model for detecting network traffic anomalies in wireless distributed ad hoc networks. Within the framework of this study, methods and algorithms of data mining and machine learning were applied. The proposed approach to traffic monitoring in wireless distributed ad hoc networks consists in the implementation of two stages: initial traffic analysis to identify anomalous events and subsequent in-depth study of cybersecurity incidents to classify the type of attack. Within the framework of this approach, the corresponding models are constructed based on ensemble methods of machine learning. A comparative analysis and selection of the most efficient machine learning algorithms and their optimal hyperparameters has been carried out. In this paper, a formalization of the traffic anomaly detection model in distributed wireless ad hoc networks is carried out, the main quantitative metrics of network performance are identified, a generalized algorithm for detecting traffic anomalies in mobile ad hoc networks is presented, and an experimental study of the network segment simulation is carried out from the point of view of performance degradation during the implementation of various network attack scenarios. Network distributed denial of service attacks and cooperative blackhole attacks have the greatest negative impact on the performance of the mobile ad hoc network segment. In addition, the network simulation results were used to build a machine learning model to detect anomalies and classify types of attacks. The results of a comparative analysis of machine learning algorithms showed that the use of the LightGBM method is the most effective for detecting network traffic anomalies with an accuracy of 91 %, and for determining directly the type of attack being carried out with an accuracy of 90 %. The proposed approach for network anomalies detection through the use of trained traffic analysis models makes it possible to identify the considered types of attacks in due time. The future development direction of this research is the consideration of new scenarios for the emergence of network attacks and online additional training of the constructed identification models. The developed software tool for detecting network traffic anomalies in distributed mobile ad hoc networks can be used for any type of wireless ad hoc networks.

ОПРЕДЕЛЕНИЕ ПОКАЗАТЕЛЕЙ ОЦЕНКИ ЭФФЕКТИВНОСТИ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

Intelligent Transportation Systems (ITS) play a fundamental role in reducing congestion, reducing traffic accidents and improving road safety. Proper evaluation of the effectiveness of these systems is an urgent task in terms of improving the performance of the entire transport network, and the development of a set of evaluation indicators will make it possible to make managerial decisions on planning the development of intelligent transport systems. To test the hypotheses about the possibility of using indicators to assess the effectiveness of ITS, a one-sample t-test was used, during the analysis it was found that the proposed indicators «environmental friendliness and safety», «reliability of the transport network», «competitiveness of public transport» are suitable for assessing intelligent transport systems

INTELLIGENT TRANSPORT SYSTEMS AS TRAFFIC FLOW MANAGEMENT TOOL (THE CASE OF KYIV)

The low quality of public transport services, the inconvenience of routes, the long duration of trips and several other problems encourage Kyiv residents to increasingly choose a car for everyday correspondence, which, in turn, increases delays, travel time and leads to environmental pollution of territories. These problems are especially acute in large cities, where numerous industrial, labour, and cultural and household connections are within the city. There is a need to create a single efficient transport system that serves as a city and implement effective traffic flow management approaches. 
 First of all, the article defines the concepts of "traffic flow management", "urban mobility" and "urban mobility regime". Four groups of problems of urban transport systems were identified and two ways of solving them were formed: extensive and intensive. Using the instrumental case of Kyiv, the level of implementation of systems for monitoring and managing traffic flows in Ukrainian cities was evaluated. The levels of the legal framework regulating the urban mobility of the population in Ukraine are described. The characteristics of intelligent transport systems and their key subject areas have been studied. Commonly accepted services for users of intelligent transport systems (ITS) are distinguished and segmented according to the needs of these system users. Features, methods of application and technologies of intelligent transport systems are presented. Services that are a priority for potential users of intelligent transport systems in Ukrainian cities are highlighted. The world experience of the formation and development of intelligent transport systems, as well as European programs for their implementation, is characterized. A SWOT analysis of the implementation of intelligent transport systems in Kyiv was carried out and recommendations were made for the creation of an ITS implementation program in Kyiv. 
 The information base of the article consists of publications by prominent Ukrainian and foreign researchers, data from the Tom Tom transport analytics service, the current legal framework of Ukraine, which regulates urban transport systems, and urban development strategies. The following methods of scientific research are used in the research process: dialectical method; method of scientific abstraction, analysis and synthesis; method of induction and deduction; comparative method; method of systematization and graphic method.

Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks

Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane and object detection algorithm barriers for smart traffic are proposed. We first convert the distorting image caused by the camera and use a threshold algorithm for the lane detection algorithm. The image with a top-down view is then determined through the extraction of a region of interest and inverse perspective transform. Finally, we implement the sliding window method to determine pixels belonging to each lane and adapt it to a quadratic equation. YOLO algorithm is suitable for identifying many types of obstacles for identification problems. Finally, we use real-time videos and the TuSimple dataset to perform simulations for the proposed algorithm. The simulation results show that the accuracy of the proposal for detecting lanes is 97.91% and the processing time is 0.0021 seconds. The accuracy of the proposal for detecting obstacles is 81.90%, and the processing time is 0.022 seconds. Compared with the traditional image processing method, the average accuracy and execution time of the proposed method are 89.90% and 0.024 seconds, which is a strong antinoise ability. The results prove that the proposed algorithm can be deployed for self-driving car systems with a high processing speed of the advanced network.

Motif discovery based traffic pattern mining in attributed road networks

With the development of intelligent transportation systems, clustering methods are now being adopted for traffic pattern recognition to discover the time-varying laws in road networks; this had attracted significant attention from the industry and academia over the past decades. Existing methods mainly focus on the mobility pattern and spatiotemporal dimension, ignoring the complex relationships among these segments in road networks. The main issues can be divided into two categories: deep integration of the structural and attribute information; global spatial dependencies for clustering structural properties. To address these issues, a clustering method for motif-based attributed road networks is proposed. A higher-order connectivity model based on motif discovery is designed, and a weighted matrix of adjacent segments is defined in the road networks. Moreover, a clustering model for motif-based attributed road networks is constructed, considering the joint relationship between node structure and features. In this study, a set of experiments were conducted on two real-world datasets. The results indicated that the performance of the proposed method is superior to that of the state-of-the-art methods.

Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.

AUS farkındalığı ve yaygınlaşmasında dünya AUS kongrelerinin etkisi

In today's modern economies, congresses, conferences and fairs are the most suitable environments for the establishment of personal and long-term commercial relations. Since such meetings provide face-to-face interaction, they provide a high level of influence and information exchange. Types of meetings and congresses are classified according to the number of participants and delegates, as well as their purpose and the place where they are held. In this study, the importance of congresses and congress types has been tried to be explained, and detailed information is given with the origin and development of intelligent transportation systems. Today, smart transportation systems offer various solutions with new technologies in order to eliminate factors such as the increasing number of vehicles, traffic density, and the increasing noise and environmental pollution problems associated with them. In addition, thanks to the smart transportation world congresses held every year, a wide audience is aware of these solutions and innovations. In our study, which topics are covered in these congresses, it is explained which interactions these congresses lead to support the formation of technological innovations and smart transportation systems awareness at the global level. With all these factors, smart transportation systems have created a growing market in the world and in Europe.

Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking

High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being used for tracking on-road objects, for which tracking-by-detection is the widely acknowledged method; however, this method is sensitive to misdetections, resulting in shortened and discontinuous object trajectories. To address this, a Joint Detection And Tracking (JDAT) scheme, which runs detection and tracking in parallel, is proposed to mitigate miss-detections at the vehicle detection stage. Road users are first separated by moving point semantic segmentation and then instance clustering. Afterwards, two procedures, object detection and object tracking, are conducted in parallel. In object detection, PointVoxel-RCNN (PV-RCNN) is employed to detect vehicles and pedestrians from the extracted moving points. In object tracking, a tracker utilizing the Unscented Kalman Filter (UKF) and Joint Probabilistic Data Association Filter (JPDAF) is used to obtain the trajectories of all moving objects. The identities of the trajectories are determined from the results of object detection by using only a certain number of representatives for each trajectory. The developed scheme has been validated at three urban study sites using two different lidar sensors. Compared with a tracking-by-detection method, the average range of object trajectories has been increased by >20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections.

Digitalization in the single transport space of the EAEU

The phenomenon of digitalization contributes to the implementation of the strategic national goals of states and international associations. The members of the Eurasian Economic Union form a single digital space of the EAEU, implement the processes of digitalization, digital logistics, the development of intelligent transport systems, and the introduction of electronic transport documents.

A CFAR Algorithm Based on Monte Carlo Method for Millimeter-Wave Radar Road Traffic Target Detection

The development of Intelligent Transportation Systems (ITS) puts forward higher requirements for millimeter-wave radar surveillance in the traffic environment, such as lower time delay, higher sensitivity, and better multi-target detection capability. The Constant False Alarm Rate (CFAR) detector plays a vital role in the adaptive target detection of the radar. Still, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, we propose and discuss a CFAR detection method, which transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler Matrix (RDM) data, to improve the detection ability of radar for moving targets such as pedestrians and vehicles in the traffic environment. Compared with conventional methods, simulation and real experiments show that the method breaks through the reference window limitation and has higher detection sensitivity, higher detection accuracy, and lower detection delay. We hope to promote the detection application of millimeter-wave radar in road traffic scenes.

TCSA: A Traffic Congestion Situation Assessment Scheme Based on Multi-Index Fuzzy Comprehensive Evaluation in 5G-IoV

The traffic congestion situation is an important reference indicator for the orderly control and management of traffic systems. As intelligent transport systems (ITS) become increasingly popular, the challenge of realizing real-time traffic congestion situation assessments (TCSAs) in the post-traffic era is particularly important. In this study, we propose a TCSA scheme for multi-metric fuzzy integrated evaluation based on three predicted vehicle traffic parameters for the 5G Internet of Vehicles (5G-IoV) environment, which is dedicated to accelerating the development of ITS. Firstly, the scheme uses dynamic multi-model adaptive exponential smoothing (DMMAES), which can calculate the optimal smoothing coefficients and weight of each model based on historical prediction errors to predict the average speed and traffic volume and then calculate the predicted traffic speed, traffic flow density, and road saturation of the three traffic congestion indicators. Secondly, the predicted values of the three traffic congestion indicators are used as fuzzy comprehensive evaluation, taking into account the vagueness of the traffic congestion levels, the uncertainty of the indicators, and the conflict among the indicators, using a trapezoidal affiliation function to determine the degree of affiliation of each indicator through the adaptive CRITIC method to determine the weights. Finally, the predicted traffic congestion situations are classified into five levels. The effectiveness of the scheme was verified by the measured data of Yanta North Road in Xi’an. The results showed that the traffic congestion level predicted by TCSA was basically consistent with the actual situation and had a high prediction accuracy.

A security framework for QaaS model in intelligent transportation systems

Recent advances in the development of intelligent transportation systems (ITSs) impose complex services that utilize Query-as-a-Service Model in ITS microsystems. Such model is vulnerable to a vast range of security threats such as Man-in-the-Middle attacks. Intelligent sensors and microsystems provide important systems-level functionalities to smart cities applications, which enhance data acquisition and system control services. This paper proposes a communication framework that handles intrusion threats to intelligent sensors during the data acquisition and service provision phases. The contributions of this research are: (1) Proposing a reliable Query-as-a-Service communication model based on Fog computing architecture, (2) Proposing communication protocols that preserve the integrity of exchanged data through intelligent sensors, and (3) Providing a security analysis based on the mobile nature of vehicles in ITS microsystems. Our proposed methodology is a data-driven one, in which entities exchange data models instead of the data itself, thus, minimizing the communication overhead and providing a smart way to tolerate misinformation. We have conducted experiments to analyze the impact of failure rate and the size of exchanged data on our proposed framework. In addition, the computational cost has been tested against the amount of communicated data reports. The results indicated that the proposed framework showed high performance in terms of the impact of data granularity on the failure rate and computational cost. Our proposed methodology achieved 89.6% detection rate, 3.5% false-detection rate, and <0.02 probability of query failure. Accordingly, our proposed framework overcomes the major limitations of traditional cloud-based model.

Efficient Resource Allocation for Multi-Beam Satellite-Terrestrial Vehicular Networks: A Multi-Agent Actor-Critic Method With Attention Mechanism

With the rapid development of intelligent transportation systems, there is an increasing demand for a variety of vehicular services, such as automated driving assistance, emergency alert, infotainment, etc. However, in some situations (e.g., remote areas or maritime scenarios), the terrestrial networks alone cannot serve the vehicular applications very well due to the infrastructure deployment and maintenance issues. Satellite networks have become an effective supplement to terrestrial networks, which complement well in terms of coverage, flexibility, reliability, and availability. In this paper, we consider the low orbit multi-beam satellite-terrestrial networks to serve for vehicles. We model this problem as a cooperative multi-agent reinforcement learning process, where each beam acts as an agent, and the global bandwidth is cooperatively shared among all the agents. A multi-agent actor-critic method with attention mechanism is proposed to allocate resources for vehicles with strict delay requirements and minimum bandwidth consumption. When allocating bandwidth, the channel efficiency, the angle of the beams and the priorities of requests in different regions are also considered. Centralized training and distributed execution is performed in the training of the agents. Extensive simulation results verify the effectiveness of our proposed method, where all the agents can well cooperative to achieve efficient resource allocation on-demand for the vehicles under strictly limited bandwidth resources.

Recurrent Neural Network Based Collaborative Filtering for QoS Prediction in IoV

As the emerging paradigm that is believed to be conducive to the development of intelligent transportation systems (ITS), Internet of Vehicles (IoV) is constructed with a number of connected heterogeneous vehicle devices which provide a variety of services. As the number of vehicle devices in IoV is growing fast, selecting the appropriate service from candidate services which are functionally equivalent is becoming an imperative task. Predicting the non-functional attribute of service invocation, namely quality of service (QoS), to ensure the optimal service selection is the mainstream direction. Considering that most of the conventional prediction methods neglect the fact that QoS values change dynamically with some objective factors, this paper proposes a recurrent neural network based collaborative filtering method called RNCF for QoS prediction. Specifically, a multi-layer GRU structure is incorporated in the framework of neural collaborative filtering to model the dynamic state of physical environments or network conditions and share the invocation records across different time slices. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the proposed QoS prediction model RNCF.

Comprehensive driver behaviour review: Taxonomy, issues and challenges, motivations and research direction towards achieving a smart transportation environment

The aim of this article is to review and analyse previous academic articles associated with car behaviour analysis for the period of 2010 to June 10, 2021 and understand the benefits of using data collection devices. Articles related to car driver behaviour and sensor utilisation are systematically searched. Three major databases – ScienceDirect, IEEE Xplore and Web of Science – were searched. A set of inclusion and exclusion criteria were developed for the search protocol. All articles were coherently classified via taxonomy. Also. The motives that have led researchers to continue their investigations are explored. The challenges and issues of driver behaviour analysis are illustrated with respect to power consumption, data analysis, detection, cost, security and privacy, sensor usage and individual challenges. The research direction of this review points towards different aspects based on the critical analysis of the different scenarios of driver behaviour studies in real time situations. Here, the critical behaviour analysis of intelligent transportation system development is addressed. The gaps in the reviewed articles include the following: sensors used during experiments, the effect of thresholds on labelling processes or data balancing and classification accuracy, the thresholds in identifying driving styles in the car-following model, insufficient experiment size (large scale or small scale) and limitations in data pre-processing. An implementation map depicting the steps of the case study is provided to give insights into the procedures and the problems they address. This review is expected to offer valid and clear points, contributing to the enhancement of driver behaviour research.

Development of Parallel Algorithms for Intelligent Transportation Systems

This paper deals with the creation of parallel algorithms implementing macro-and microscopic traffic flow models on modern supercomputers. High-performance computing contributes to the development of intelligent transportation systems based on information technologies and aimed at the effective regulation of traffic in large cities. As a macroscopic approach, the quasi-gas-dynamic traffic model approximated by explicit finite-difference schemes is proposed. One- and two-dimensional variants of the system are considered, and the concept of lateral velocity and different equations for obtaining it are discussed. The microscopic approach is represented by the multilane cellular automata model. The previously developed model is extended to reproduce synchronized flow in accordance with Kerner’s three-phase theory. The new version starts from the Kerner–Klenov–Schreckenberg–Wolf model and operates with the concept of the synchronization gap. Macroscopic models are relevant for determining the common characteristics of road traffic, while microscopic models are useful for a detailed description of cars’ movement. Both approaches possess inner parallelism. The parallel algorithms are based on the geometrical parallelism principle with different boundary conditions at interfaces of the subdomains. Sufficiently high speedups were reached when up to 100 processors were involved in calculations. The proposed algorithms can serve as the core of ITS.

Blockchain-based anonymous authentication for traffic reporting in VANETs

The application of vehicle ad-hoc networks (VANETs) has brought huge potential to the development of intelligent transportation systems. By traffic event reporting, traffic efficiency can be significantly improved. However, people usually lack the motivation for reporting traffic events or driving violations, because once the identity of the reporter is leaked, he or she may be tracked by malicious attackers or be retaliated by violators. However, if the reporter is anonymous, it is difficult to guarantee the accuracy and authenticity of the reports. In order to solve this contradiction, a mechanism that combines certificateless message authentication and blockchain incentives is proposed in this paper. A certificateless message signature algorithm is designed to provide the anonymity and non-repudiation for traffic-related message reporters. Simultaneously, an adaptive t-threshold multi-signature mechanism is introduced in our scheme. Similar to crowdsourcing, it requires multiple participants to witness the authenticity of the message together. Aggregation signature verification improves message authentication efficiency. A trust currency called TCoin is introduced to motivate reporters' enthusiasm for participation in intelligent transportation. The security analysis and simulation results show that our scheme is secure and effective in the intelligent transportation system.

Research on a Prediction Method for Passenger Waiting-Area Demand in High-Speed Railway Stations

The rapid development of intelligent transportation systems and high-speed railways has shortened the waiting time of passengers and the demand for waiting areas. Large-scale stations not only increase the difficulty for passengers when traveling but also waste a great deal of land resources and construction funds. Therefore, this research analyzes passenger waiting area demand according to the characteristics of urban development, passenger travel characteristics and station departure passenger flow. This paper establishes a prediction model for the number of passengers spending time in the waiting room, taking into account passenger traffic and train departure timetables. We used Beijing South Railway Station, Xi’an North Railway Station, Hefei South Railway Station and Zhoukou East Railway Station as examples to predict the numbers of passengers spending time in waiting rooms of different types and scales of station. Research results show that shortening the length of passengers’ early arrival times can effectively reduce the number of passengers gathered in medium-sized stations, which are located in new first-tier cities. Under the influence of the urban traffic environment and passenger flow, the uncertainties regarding travel time and passenger flow in first-tier cities are lower than those in new first-tier cities and higher than those in third-tier and fourth-tier cities. Therefore, the waiting area demand of passengers departing from medium-sized stations in new first-tier cities is lower than that of passengers from large stations in first-tier cities, and higher than that of passengers from small stations in third-tier and fourth-tier cities.

Factors affecting intelligent transport systems towards a smart city: a critical review

Intelligent Transportation Systems (ITS) is one of the basic components of a smart city (SC); however, very few studies have examined the impact of surrounding factors on the effectiveness and development of ITS. This study aims to identify the core factors affecting the development of ITS in Vietnam and as a result, form a branching database to approach the standards of SC. To achieve the stated purpose, materials are collected for research in developing and developed countries which comes from official sources including scientific articles, conference papers, books, and opinions from experts (from 2010 to 2020). This study identified 28 factors affecting the development of ITS projects. These factors are divided into 5 main groups namely the lack of undivided attention from the government, financial constraints, inadequate/incomplete transport infrastructure, the over-development of urbanization, the readiness and integration for ITS, which are analyzed depending on their influence. This study adds to the body of knowledge by offering new insights and contributes toward a greater understanding of mobility in a smart city. Furthermore, to guarantee sustainable ITS development, the digital infrastructure ecosystem should be extensively discussed. As a result, it created a useful measurement tool for government agencies, planners, and traffic system designers to help them self-assess and make action plans now or in the near future.