РАЗВИТИЕ АРХИТЕКТУРЫ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

Problem: Services that use ICT (Information and Communications Technologies) have been developed to improve the mobility in cities; however, especially in developing countries, these services are not often based on adequate reference architectures, such as ITS (Intelligent Transport Systems) architectures, which prevent integration and interoperability. Objective: Propose a development process for the design of mobility services in an intermediate city of a developing country, based on an ITS architecture formulated particularly considering context. Methods: The reference ITS architectures and the particular context of a Colombian intermediate city are reviewed, in order to identify which is the best process to adapt an ITS architecture to these type of cities. With the process identified, the ITS architecture for Popayan (Colombian intermediate city) is designed and finally, the design of the services based on it, is carried out. Results: The methodology developed for the design of the ITS architecture and the architecture designed particularly for Popayan are summarized. Following, the design of two mobility services (“Public transport vehicle tracking” and “Traffic measurement”) for that city based on architecture is detailed. Conclusions: The particular environment of an intermediate city and its priorities allows to determine the services to select for its ITS architecture from a reference architecture. ITS architecture development of a city allows the incremental development of services that really improve their mobility in a sustainable manner.

The paper presents an analytic approach to generate the market packages for the operation via the intelligent transportation system (ITS) architecture. This analytic approach uses the concepts of the "use case diagram" and "sequence diagram" defined by the unified modeling language (UML). By following the steps, we take Taiwan's public bus service as an example and generate the market package effectually, public bus monitoring and public bus scheduling and dispatching, which is corresponding to the real world situation. We also point out the academic models in market packages that could be the patent products. Consequently, driving the establishment of ITS market package not only conduces to ITS development but also promotes the knowledge economic in our society.

Automated Vehicles (AVs) are expected to help in significantly reducing traffic injuries and fatalities in the near future. However, to achieve this goal, they must be safe and secure. The recent news of the first fatal crash of AV including pedestrian confirm the urgent need of addressing AV safety and security issues to prevent such accidents from happening in the future. In order to outperform human drivers, AVs need to communicate with the other traffic participants, which makes them more vulnerable to cyberattacks. Cooperative Intelligent Transport Systems (C-ITS), which include vehicle-to-vehicle and vehicle-to-infrastructure communications, are expected to be launched in Europe next year. Thus, assuring their safety and security is crucial. This paper presents an approach, CESAM&SSM, for modelling safe and secure C-ITS using the CESAM method and the Six-Step Model. A combination of these two methods enables comprehensive analysis of C-ITS from operational, functional, constructional, safety, and security perspectives. The propose approach is compliant with three international standards: ISO 26262 – vehicle safety standard, SAE J3061 – vehicle cybersecurity standard, and ISO 21217 – intelligent transport system architecture standard.

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

In this paper I perform an analysis of vehicle lane changes and how they relate to fastest path determination. I converted live discrete loop detector data from the California Department of Transportation into continuous data to be utilized by vehicles in a vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system (ITS) architecture. The continuous data was then used by FreeSim (http://www.freewaysimulator.com) to simulate live traffic conditions. As the time to traverse the edges in the transportation network were being constantly updated, additional vehicles were inserted into the network to determine travel times and fastest paths from a source node to a destination node. The output shows that faster and more accurate paths can be found if lane data is obtained rather than just summary data of loop detectors. Further, if vehicles can be routed along paths with optimal lane changes to decrease the total travel time, a savings of approximately 33% of the travel time can be experienced. It is also shown that the number of lane changes needed in the fastest path with regards to lanes is lower than the number of lane changes needed for other candidate fastest paths, and highways with less congestion require fewer lane changes.

Worldwide intelligent transportation systems (ITSs) are at the edge from research and development to deployment and commercial operation. Japan has already introduced ITS technology in the market and the USA and the European Union have elaborated planes for deployment. ITS in this context means communication and services based on communication technology between vehicles and other traffic participants on one hand and traffic and road infrastructure, vehicle manufacturer, and other mobility service provider on the other hand. For a successful introduction, a reliable and secure exchange of mobility‐related information is a key factor. To provide such an exchange for an overall architecture for ITS and for all participants and users is necessary. With the introduction of the market, the assessment of an ITS architecture without an existing reference system arises. Since most assessment methods currently existing, measure how ‘good’ an architecture is in comparison with another architecture. In this study, the authors describe ways and approaches how existing methods can be extended and combined to provide means for the assessment of ITS architectures in the pre‐deployment phase. As a result, deploying parties should be enabled to assess an architecture before introducing it to the public.

In this paper, we will outline the current European development of wireless communications to support cooperative Intelligent Transport Systems (ITS). The focus will be on spectrum allocation and the physical and medium access control layers of the ITS-G5 access technology, which is under specification by the European Telecommunications Standards Institute (ETSI). The European ITS architecture and European standardization will also be briefly discussed.

Intelligent Transport Systems (ITS), and the specialised subset of them represented by Cooperative ITS (C-ITS), are part of the wider machine-to-machine communications, and software driven world. The aims of ITS and C-ITS are multifold but for the purposes of this book are mainly in improving safety of transport users and their transport modes. The chapter that follows covers some of the basics of security, including the contrast with safety and privacy, the role of privacy protection in the ITS context, and a review of the cryptographic countermeasures recommended for C-ITS using the certificate and signature schemes defined in IEEE 1609.2.

Intelligent Transport Systems (ITS) and Cooperative Intelligent Transport Systems (C-ITS) are of high significance, mainly due to the benefits they have in terms of operation of the transport network. Despite ITS and C-ITS importance in the operation of the transport network, there is a major knowledge gap regarding their development, way of operation and significance worldwide and especially among the responsible for their deployment public authorities. In order for such fragmentations to be tackled, an online training platform concerning the operation and impacts of ITS and C-ITS has been designed in the framework of the European Union's Horizon 2020 funded CAPITAL project. For the proper design of the CAPITAL Online Training Platform, two main approaches have been studied: capacity building and massive open online courses. The present study provides insight regarding the design and the context of the CAPITAL Online Training Platform.

The transportation sector, and in particularly intelligent transportation systems, generate large volumes of real-time data that needs to be managed, communicated, interpreted, aggregated, and analyzed. To this end, innovative big data processing and mining as well as optimization techniques, need to be developed and applied in order to support real-time decision-making capabilities. Towards this end, this paper presents an ETL (extract, transform and load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented here, is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. The proposed architecture relies on the adoption of “big data” technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators. The proposed architecture is capable of handling real-time and historical data using big data technologies such as Spark on Hadoop and MongoDB. The DATEX-II data model is adopted, in order to harmonize traffic data provided by the highway operators. The work presented here, is still part of ongoing work currently addressed under the EU H2020 OPTIMUM project. Preliminary results achieved so far do not address the final conclusions of the project, but enabled us to demonstrate considerable gains in performance, when compared to other traditional ETL approaches, and also form the basis for pointing out and discuss future work directions and opportunities in the area of the development of big data processing and mining methods under the ITS domain.

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures.

Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have recently been trying to find their place in ITS security strategies. These approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory and ontologies. In conclusion, a correlation is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

By 2020, there will be more than 24 billion smart devices connected in the Internet of Things (IoT). Tremendously augmented motorization, population, and urbanization has not only brought us many amenities but also has increased traffic congestion to its limits. In this paper, we used IOT to design an efficient and congestion free Intelligent Transport System (ITS). A lot of research is done to either improve or change any one aspect of ITS at one time. This paper demonstrates every aspect or features of an efficient ITS. The purpose of this research is to provide developing countries a detailed and easy to follow ITS architecture so that they can create an ITS for their populace.

The United States in the early 20th century became the first nation to introduce the concept of intelligence in the transportation systems followed by intensive research and development by the European Countries like (Sweden, Switzerland), Japan, South Korea in the similar area. Since 1991, Intelligent Transport Systems (ITS) have been an emerging and promising area of research. This fact is also aided by the recent advancement and developments in the field of automated driving systems over the past few years. Cooperative Intelligent Transportation Systems (C-ITS) is the latest advancement over the existing Intelligent Transportation Systems (ITS) as now the systems are making use of communication and interaction with infrastructure using connected and automated vehicles (CAV’s) in order to create helpful and exciting applications that concern road safety, environment, mobility. Trust and Node Cooperation are the most crucial factors on which C-ITS relies as the decisions that are taken by a vehicle are based on the data and information it gets from the network infrastructure that is installed on the roadsides which is basically a pool of information which has been gathered from other vehicles and smart infrastructures that are part of the VANET. Presence of any misbehaviour in the network or a compromised node in the transport network system will lead to atrocious results for both traffic and network safety. Detecting any misbehaviour in the network can save us a lot of resources that will go into creating a system that can defend the complete network against a threat. Through the medium of our thesis project we will be detecting misbehaviour in the system using Deep Learning techniques. A most Basic MLP Neuron Architecture based Artificial Neural Network (ANN) would serve as our baseline and proposed models include LSTM and Transformer Model which will be used for time series classification.

Emerging intelligent transportation systems (ITS) are based on several technologies. A new concept of integrating visible light communications (VLC) in ITS is introduced in this paper. VLC in ITS is a cost effective method of implementation. This paper presents a VLC broadcast system considering LED-based traffic lights. It discusses the integration of traffic light road side unit (RSUs) within the existing ITS architecture. A prototype demonstrator of the designed VLC systems is also presented. A robust modulation technique based on direct sequence spread spectrum (DSSS) sequence inverse keying (SIK) has been implemented to minimize the effect of noise sources. Experimental results show data communication range of over 40m with 200mm custom designed traffic light, even during bright sun light.