Key Issue in ITS Deployment: System Integration

To help transportation planners, the Federal Highway Administration (FHWA) has recently sponsored the development of a sketch planning analysis tool for estimating the benefits and costs of intelligence transportation system (ITS) deployment. Called the ITS Deployment Analysis System (IDAS), the tool was developed by Cambridge Systematics for FHWA and operates as a postprocessor to traditional planning models that are based on the four-step planning process. IDAS was used to predict the likely benefits of deploying ITS in Chittenden County, Vermont, a medium-sized region in northwestern Vermont, to gain insight into IDAS’ applicability in evaluating ITS benefits. A sensitivity analysis was also conducted to assess the sensitivity of IDAS’ results to the choice of values of some of the model’s parameters. The sensitivity analysis demonstrates that some of the model’s parameters tend to have a more significant impact on the results than others. Finally, the results indicate that, for medium-sized areas similar to Chittenden County, coordination of signals along major arterials and transit projects for automatic vehicle location and scheduling appear to be more cost-effective than freeway and incident management systems.

A perspective on ITS deployment

This paper examines the relationship between public and private investments in intelligent transportation system (ITS) technology and the effects on transportation performance through the highway system. ITS technology is an infrastructure that promotes integrated information sharing that can be used to improve system performance. ITS combines better infrastructure with information and control technologies to ( a) reduce traffic congestion by maintaining traffic flow, ( b) reduce transportation-generated pollution, ( c) improve transport efficiency, and ( d) produce economic benefits. A statistical analysis of partial least squares structural equation modeling was used to test three hypothesized relationships. Primary data were collected from U.S.-based commercial vehicle transportation service providers. Respondents were parceled with a geographic information system analysis of secondary data from the U.S. Department of Transportation National ITS Deployment Tracking Survey. On the basis of their level of regional ITS experience, primary data respondents were matched to regions and split between regions with low ITS experience and high ITS experience as a proxy for regional public investments in ITS. Performance measures were based on efficiency, reliability, responsiveness, quality, carbon emissions reduction, and equipment utilization for commercial vehicle operators. Results indicate that private investments in ITS significantly positively affect the responsiveness performance measure for commercial vehicle operators in regions with high public ITS investments. The same relationship is not significant in regions with low public ITS investments, which indicates at least some transportation performance improvements for regions with higher investments in ITS. Implications for research and practice are discussed.

Recent years have seen a more intensified deployment of Intelligent Transport Systems (ITS) in Polish cities. Work is also underway on the implementation of ITS on the national roads within the National Traffic Management System (KSZR). The research project RID-4D (part of Road Innovations), is designed to fill the gap, which is the lack of systematics of ITS services in Poland. The paper presents a proposal of ITS services systematics, which was developed based on an analysis of key international standards. FRAME architecture and American standards were the basis for the preparation of a standard scheme of ITS services. The results of the work on the KSZR architecture as well as local and regional architectures of urban systems were also taken into account.

Deployment of intelligent transportation systems (ITS) is one of the 12 key areas of research and development in China's 10th five-year plan, lasting from 2000 to 2005. China's highway system has grown significantly over the last decade. Traffic congestion and air pollution have been the two major problems facing Beijing for decades and are the two central concerns for the successful execution of the 2008 Olympics. Recently, Beijing's government has made tremendous efforts toward solving those problems, and ITS technology will likely play an important role in the solutions. This installment deals with ITS issues in China. Clearly, ITS will play a role in the future, and all major cities are working on integrating systems into their current scenarios. Along with this, we're witnessing the boost of China's economy and, particularly, its great effort constructing its new transportation network. This installment presents the architecture and the technology that will be the starting point for the construction of the ITS, that will be used in Beijing 2008 Olympics.

Through research and lessons learned from early intelligent transportation systems (ITS) deployers, it has been concluded that the procurement of goods and services to support ITS deployments represents a major obstacle for transportation agencies responsible for deploying ITS. This obstacle can be attributed to the challenges associated with the procurement of goods and services to support the deployment of complex information technology (IT) systems. In response to this challenge, innovative procurement methods have been established. In an effort to aid state and local transportation agencies with the identification of appropriate innovative procurement methods, the National Cooperative Highway Research Program (NCHRP) funded Project 3-77 with the objective of developing a guide to ITS procurement. A research team was established to develop the guide and an NCHRP research panel was established to oversee the development of the guide. As a result of this research it was determined that project, agency and environment characteristics and systems engineering and project management principles are key inputs into the process used to identify an appropriate procurement approach for a specified ITS project. A decision model was developed to represent this process.

Aim. Threats of a non-physical nature have a significant effect on the security of intelligent transportation systems (ITS). They may have the form of unlawful interference in the development and implementation of ITS. The creation of a trusted framework for the development and implementation of ITS is examined as in the case of intelligent water transportation systems (IWTS). Problems. By decree of the President of the Russian Federation, ITS, telecommunications and security of information processing are among the priority areas of scientific and technological development. ITS technologies, as well as those involved in the creation of trusted, secure system and application software are among the most important critical high technologies. The operation of ITS involves wide use of computerised systems that implement the latest information and telecommunication technologies, automated and automatic control technologies, artificial intelligence that can pose security threats. ITS are to be developed and operated in a trusted environment. Methods. The paper used the methodology for ensuring the security of IWTS, development of secure hardware and software platforms for secure automated systems, methods of system analysis, dependability theory, information protection, and law. Results. The paper defines the problem of creating a trusted framework for the development and implementation of IWTS, the applicable terminology is developed. The author examined the effect of IWTS on the security of critical information infrastructure (CII) and national security, developed a model of relationships between the IWTS security domains taking into account threats of physical and non-physical origin. Examples of computer incidents within IWTS that caused consequences at the national and international levels are given. The composition of the IWTS facilities attributed to CII is defined, critical processes implemented by standard CII facilities as part of the IWTS are set forth. The author lists conceptual problems of IWTS security, defines the principles of creating a trusted framework for the development and implementation of IWTS. Conclusion. Ensuring the security of IWTS against modern threats requires solving a number of problems associated with the creation of a trusted framework for the development and implementation of IWTS. For the purpose of improving the timeliness and quality of their solution, the paper proposes an intuitive terminology that reflects the subject area and helps finding a common understanding of the security domain by experts from various industries. IWTS facilities have an effect on CII security and national security in general. That is taken into account in the model of relationships between IWTS security domains, demonstrated using cases of computer incidents within IWTS that caused consequences at the national and international level. Given the above, the paper lists IWTS facilities attributed to CII and sets forth examples of critical processes implemented by standard CII facilities as part of the IWTS. The defined list of conceptual IWTS security problems take into account the growing landscape of IWTS security threats that includes insecure software, hardware and software platforms, software and hardware systems and emerging technologies. When developing the principles for the creation of a trusted IWTS development and implementation framework, the author took into account the best practice of implementing the methodology for creating national secure hardware and software platforms of CII facilities that enables the creation of secure automated systems for various applications that are based on domestically-developed solutions. The examined matters are of a systemic nature, which allows using the findings in the development and implementation of ITS in other modes of transportation.

The potential complexity and size of Intelligent Transport Systems (ITS) requires that they be implemented through a systems engineering approach based on the use of ITS Architectures. These enable a high level set of “views” of the proposed ITS to be obtained early in its lifecycle so that many of the details and implications can be checked and, if necessary, changed at significantly less cost than if the need for a change is only found when some/all of the development work has been completed. The FRAME Architecture has been created for use as the starting point for any deployment of ITS, and a methodology for its use has been developed. This methodology is now supported by two FRAME Architecture Tools. The FRAME Architecture has now been extended to include cooperative systems.

Following the newly adopted European Directive on Intelligent Transport Systems (ITS) 2010/40/EU, officially published on November 30th 2023 under the new code (EU) 2023/2661, opportunities arise for the entire industry. The main aim of the Directive is to promote coordinated deployment of intelligent transport systems in accordance with technological development and user needs. It provides a framework for developing and implementing technologies to improve transportation systems' efficiency, safety, and sustainability within the European Union (EU) and member states. The new European legislation should ensure that ITS applications in the field of road transport enable seamless integration with other modes of transport thus facilitating a shift to those modes whenever possible. This will in time improve the safety, efficiency and accessibility of the European transportation system. In this paper, we are analyzing the main objectives of the EU initiative and changes compared to the initial Directive of 2010 with special regard to changes in Priority III, safety and Priority IV, ITS services for connecting, cooperation and automatization of mobility. Although all key changes are analysed the special focus was on critical services such as road safety-related traffic information services, to be made available across Europe.

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.

The deployment of Intelligent Transportation Systems (ITS) is a challenge for industry and scientific community. Traffic characteristics change widely within a single day, therefore, ITS engineers and researchers must deal with that dynamic behavior. On the other hand, once the ITS depends on networking services, specific studies are required to consider the communication parameters along with vehicle mobility. In this paper, we propose an analytical model based on the Stochastic Petri Net (SPN) theory for evaluating Vehicular Ad-Hoc Networks (VANETs) infrastructures, considering mobility and network parameters, and the respective constraints. We employ expolynomial distributions to represent Roadside Unit (RSU) service rates. Those probability distributions allow the approximation of many analytical and empirical data. Results show that parameters such as vehicular density, message frequency, and RSU radius may affect significantly the overall system performance.

The purpose of this panel is to introduce the safety and security issues related to the development and operation of Intelligent Transportation Systems (ITS) to Compass participants. Many of these issues need to be addressed by the system safety and computer security communities prior to the development and deployment of ITS. For example, how can information technology be applied in the context of a fully automated highway system (AHS) such that the safety, security, and performance of the system are not compromised? At present, the US and other countries are funding academia and industry to build prototype automated highway systems in which vehicles are controlled via drive-by-wire technology, with vehicles traveling at high speeds (in excess of 30 m/s) at close spacing (1 to 4 m). The potential impact of software errors or hardware errors on system safety and security are great.

Intelligent Transportation System (ITS) refers to the use of information technologies to address and alleviate transportation problems. The challenges of ITS exist mainly in synthesizing information from geographically distributed, dynamic and heterogeneous databases, specialized sensors and other systems. This synthesis process is computation and communication intensive. Computational Grid is a promising platform for such large-scale data-intensive applications. Building on grid computing technologies, this paper presents a novel infrastructure referred to as Traffic Information Grid (TIG) for ITS development and deployment. The service-oriented and layered system architecture of TIG is introduced and described. Two implementation modes-request/response mode and subscribe/notify mode are presented and evaluated with real traffic data respectively. In addition, an example application is given to illustrate how the TIG works. Based on the practice of TIG, Grid computing is proved to be an effective solution for data-intensive applications.

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Recent studies showed that one of the major environmental problems is the transport-related air pollution and road transport alone is expected to be the largest contributor to anthropogenic climate forcing in 2020. The development of more efficient vehicles, the use of alternative energy sources, and the deployment of intelligent transportation systems (ITS) are all solutions toward the decarbonization of the sector. In this chapter, an energy-oriented driving assistance system focusing on the assessment of the current driving style is proposed. In fact, it has been observed that a change of the driving style may provide savings from 5 to 40% of the total energy expenses, as well as reductions of the air pollution. The proposed system is fully integrated in a smartphone application, which acquires the signals related to the vehicle dynamics (e.g., velocity and acceleration) and computes three power-related indices containing significant information about the current driving style. Based on such indices, a feedback communication can be given to the driver (if needed) to induce a change in the driving style, which in turns would result into an energy saving. Differently from the existing studies, the proposed application is vehicle-independent and does not require any connection to the vehicle CAN-bus or OBD-interface. The effectiveness of the proposed approach is assessed via an experimental campaign carried out on urban and extra-urban routes by different drivers. Experimental results show that the proposed driving assistance system may reduce the vehicle consumption up to 30%.