Implementation Of Intelligent Transportation Systems Research Articles

АНАЛИЗ ФУНКЦИОНАЛЬНЫХ ИНДИКАТОРОВ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ КАК ПОКАЗАТЕЛЕЙ ПОВЫШЕНИЯ ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТИ АВТОМОБИЛЬНОГО ТРАНСПОРТА В КУРСКОЙ ОБЛАСТИ

The article presents an analysis of modern research in the field of assessing the anthropogenic impact of motor transport, including emissions of pollutants into the air, noise and vibration impact. When implementing measures aimed at reducing the negative impact of motor transport, considerable attention is paid to the development and implementation of intelligent transport systems (ITS). To assess the effectiveness of using ITS elements in the Kursk region in terms of improving environmental performance, the authors analyzed the following functional indicators of ITS: mass emissions of pollutants contained in vehicle exhaust gases; noise level from traffic flow; CO and NOx concentration values. The conducted field studies and calculations indicate the high efficiency of ITS implementation to reduce the environmental load on sections of the road network.

Foreign Practices of Intelligent Transport Systems Functioning: Experience of Japan and USA

The article examines the experience of managing the implementation of intelligent transport systems (ITS) in the USA and Japan, analyzes the main forms of interaction between the state, local government and private sector organizations in the implementation of relevant projects. The research used methods of comparative legal and secondary analysis of statistical data. The presented work shows that in the USA and Japan, public authorities played a key role in the process of creating intelligent transport systems, and the private sector acted as a secondary agent. At the same time, the Japanese and American models of ITS implementation management differ significantly from each other. In Japan, the main participants in this process are entities with different social and managerial status, in the United States, equal organizational structures take part in the creation process. The consequence of this is that the financing of projects to create intelligent transportation systems in the United States is carried out by the states, the federation and the private sector, and in Japan only by the state and the private sector. The presented difference leads to the fact that the Japanese model of intelligent transport systems is characterized by a vertically integrated model of various subsystems with subordination. In turn, in the USA, ITS is a network structure. It should be added to the above that both the United States of America and Japan have formed an effective communication system between the main participants in the process of implementing intelligent transport systems, which avoids various dysfunctions and contradictions in their actions. In particular, in these countries, many discussion platforms, interdepartmental commissions, as well as working groups have been organized, within which the positions of the departments involved in the process of creating ITS are coordinated, as well as contradictions that arise during the implementation of the project under consideration are resolved. The experience of advanced countries in implementing ITS can be useful, given that similar projects are actively being implemented in most regions of Russia.

Information Security and Privacy Management in Intelligent Transportation Systems

With the global digitalization of services, passenger Intelligent Transportation Systems (ITSs) have emerged as transformative components, yet the practical implementation of state-of-the-art measures to ensure information security and privacy presents substantial challenges. In this article, we propose a framework for information security and privacy management. The framework is validated through two empirical studies. First, the framework is used to extract data during the literature review defining state-of-the-art aspects and measures. Second, a survey-based analysis of running passenger ITSs in selected regions of the European Union provides insights into real-life ITS implementations, enabling a thorough comparison with the proposed state-of-the-art measures. The study also showed that the proposed framework depicts some dependencies between measures, and, thus, using its matrix structure for the state of information security and privacy management in the organization helps to cross-check the usage of policies or methodologies by the organization departments. Our findings resulted in recommendations for organizations developing ITSs to enhance their information security and privacy management systems and bridge the gap between research proposals and practical implementation.

Driver-Centric Urban Route Planning: Smart Search for Parking

With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads to an additional strain on traffic flow, while the search for parking spaces lowers the level of service. In inner cities, vehicles circulate in search of an available parking space, leading to an increase in travel time, fuel consumption, pollutant emissions, and a decrease in traffic safety. The search for a free parking space generates a significant increase in traffic in urban areas. To solve the parking search problem, it is necessary to develop certain strategies and measures that minimize circling in search of a parking space. The implementation of intelligent transportation systems stands out. By applying intelligent transport systems, drivers are provided with information about free parking spaces, which reduces the circulation of vehicles in search of free parking. Although initially ITS systems mainly provided services for closed parking lots and garages, with the further development of the system, the service was extended to street parking lots or open-type parking lots. These measures not only solve traffic challenges but also promote sustainability in urban areas. This article analyzes the effect of a cooperative approach of guiding vehicles to available parking spaces compared to a standard model of searching for an available parking space. Within the framework of the advanced model for searching for available space, four parking demand scenarios were defined and simulated. Based on the created traffic simulation, a comparative analysis was made between the classic and cooperative approach, while the primary differences are manifested in the load of the traffic flow A simulation model was developed using the road network from the urban center of Zagreb.

ВОПРОСЫ СОВЕРШЕНСТВОВАНИЯ ТРАНСПОРТНОЙ ОТРАСЛИ В УСЛОВИЯХ РАЗВИТИЯ ПОДКЛЮЧЁННЫХ ТРАНСПОРТНЫХ СРЕДСТВ

The article examines topical issues of improving the transportation industry in the context of the active development and implementation of connected vehicles (CVs). The relevance of the topic is driven by the rapid growth in the number of CVs, their increasing impact on the transportation system, and the need for a comprehensive optimization of the industry to fully realize the potential of new technologies. The main objective of the work is to identify key ways and specific measures to improve the transportation industry that will allow maximizing the benefits of CVs. To achieve this goal, a systems approach is used, which involves a comprehensive consideration of the complex interrelationships between vehicles, infrastructure, and transportation services. The implementation of Intelligent Transportation Systems (ITS) plays a crucial role in optimizing the interaction of these components. The article identifies the main opportunities and challenges associated with the development of CVs. New opportunities include improving road safety, reducing emissions, optimizing logistics and transportation, and enhancing mobility. At the same time, new challenges are emerging, including the need to ensure cybersecurity of CVs, standardization of heterogeneous systems and technologies, and their seamless interaction. The impact of CVs on key aspects of the transportation industry – safety, environment, economy, and social sphere – is analyzed in detail. Specific ways to improve safety are shown through accident prevention systems and driver monitoring systems. The potential of CVs to reduce emissions through traffic optimization and development of alternative modes of transportation is considered. Ways to increase efficiency through logistics optimization, vehicle maintenance, and driver routing are identified. Social effects in the form of increased mobility and new services for passengers are highlighted. The scientific novelty lies in the comprehensive systems consideration of a wide range of problems and ways to improve the transportation industry, taking into account the rapid development of CVs. The practical significance is the specific recommendations on applying a systems approach and implementing ITS to achieve maximum effect. Further research directions include a detailed elaboration of the proposed specific measures to maximize the benefits and minimize the risks associated with the proliferation of CVs. In particular, it is recommended to further optimize logistics processes, vehicle fleets and driver activities, accelerate the development of alternative modes of transportation and services to improve passenger comfort and safety, and establish close cooperation among all stakeholders.

Evaluation of intelligent transportation system implementation alternatives in metaverse using a Fermatean fuzzy distance measure-based OCRA model

The concept of the Metaverse, an immersive simulated world with parallels to reality, has gained significant prominence in recent times. Initially popularized through gaming, the Metaverse is now poised to infiltrate various aspects of human life. Intelligent transportation systems represent a promising yet challenging domain for Metaverse integration. Alternative implementations can create challenges in different dimensions. A comprehensive evaluation that takes challenges and opportunities for the different dimensions into account is required in decision making process of choosing the best implementation method. This study presents the development of a novel evaluation model, the Fermatean Fuzzy Operational Competitiveness Rating (OCRA) model, which incorporates the Fermatean Fuzzy Distance Measure (FF-DM) and Relative Closeness Coefficient (FF-RCC) techniques. The model is tested in a case to rank three alternative approaches, considering criteria of four key dimensions: managerial, safety, user, and urban mobility. In the first stage, the FF-DM and FF-RCC-based tool is employed to determine the criteria weights. In the second stage, an enhanced version of the Fermatean Fuzzy OCRA model, utilizing FF-DM and FF-RCC, is employed to rank the alternatives. The findings indicate that policymakers' decisions in traffic management hold the potential to shape the trajectory of the Metaverse movement, representing an unparalleled opportunity with implications that extend beyond our current comprehension.

Why Uncertainty in Deep Learning for Traffic Flow Prediction Is Needed

Recently, traffic flow prediction has gained popularity in the implementation of intelligent transportation systems. Most of the existing models for traffic flow prediction focus on increasing the prediction performance and providing fast predictions for real-time applications. In addition, they can reveal the integrity of a prediction when an actual value is provided. However, they cannot explain prediction uncertainty. Uncertainty has recently emerged as an important problem to be solved in deep learning. To address this issue, a Monte Carlo dropout method was proposed. This method estimates the uncertainty of a traffic prediction model. Using 5,729,640 traffic data points from Seoul, the model was designed to predict both the uncertainty and measurements. Notably, it performed better than the LSTM model. Experiments were conducted to show that the values predicted by the model and their uncertainty can be estimated together without significantly decreasing the performance of the model. In addition, a normality test was performed on the traffic flow uncertainty to confirm the normality, through which a benchmark for uncertainty was presented. Following these findings, the inclusion of uncertainty provides additional insights into our model, setting a new benchmark for traffic predictions, and enhancing the capabilities of intelligent transportation systems.

An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.

A Discussion on Finding the Opportunity of Intelligent Transportation Systems (ITS) Implementation in Peru Based on Economics

The means of transport are major points in a country's infrastructure and economic development, so it is important to analyse its methods and obstacles for further interventions. This study aims to discuss the Opportunity of Intelligent Transportation Systems (ITS) implementation in Peru based on the Economy. The study was subdivided into parts. In the first part briefly reviewed the system of transportation in other countries in comparison with Peru. It further discusses the technologies influencing ITS development, the barriers to ITS implementation as well as Strategies for Implementing ITS in Peru. Three research questions guided that study and an expo facto research design was adopted as the study utilizes secondary data. The study does not consider human participation, rather, kinds of literature and reports of financial status and projections were collected, collated, utilized, and analysed for the study. The study revealed that the worldwide ITS issues is more controlled that that witnessed in Peru as the issue fluctuates between 0 and 100%. Peru has a promising economy which ITS will be implemented with ease. It is concluded that a comprehensive analysis taking into account a range of economic, social, and technological factors will be required to fully understand the potential and challenges of ITS implementation in the country.

Automation of haulers for debris removal in tunnel construction

AbstractTunnel construction is one of the areas of civil engineering where automation systems can increase both safety and productivity. That is why, to promote a continuous improvement and optimization of construction processes, the implementation of intelligent transport systems and, specifically, autonomous vehicles has been proposed as a catalyst for public works construction activity, including tunnels, reducing personnel, and operation costs, as well as improving safety. Under this scope, this paper presents the development of the automation system for a 25‐ton hauler truck, focusing on the task of removing debris from public works, including tunnels. This automation system has been designed, developed, and tested under real conditions in the facilities of two companies dedicated to civil engineering works, including vehicle guidance with and without gobal positioning system, obstacle detection, and tele‐operation for load/unload tasks. The main novelty of the system is the capacity of easy integration into the production system of the works, with a capacity to switch between manual, tele‐operation, or autonomous driving modes, and with specific sensors and control systems for combining indoor and outdoor navigation. The presented work includes the description of the automation of the vehicle, the general architecture, the environment perception, and the tele‐operation capacity, including indoor navigation and obstacle detection algorithms. The proposed system has been implemented and tested in real conditions, demonstrating the performance of the system and the benefit of the automation for this application.

A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

In today’s world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management.

Intelligent Transportation Systems in Developing Countries: Challenges and Prospects

Developed countries have paved the way for the implementation of intelligent transport systems to improve the safety, efficiency, and environmental impact of transport. With developing countries entering the fray, the question is: Is ITS as implemented in developed countries relevant to developing countries? This research question is discussed in this paper for the case of sub-Saharan countries that are among the poorest countries worldwide. To this end, the paper outlines the main differences in transportation scenarios between developed and sub-Saharan countries, and then considering the main constraints of sub-Saharan countries, a guide towards an affordable intelligent transportation system in these countries is discussed. Finally, the paper proposes two main novel ideas for the deployment of intelligent transport systems on dirt roads.

Selected Indicators of Al-Najaf Road Network: Public Transportation Noise and Pollution

It is urgently necessary to improve the state of transportation and related infrastructure, especially given that the most important indicators of urban development gauge a city's progress. One of the most influential of these indicators is what is known as "smart transport, " which refers to transportation that utilizes modern communication and information technology technologies to address various challenges in various transportation sectors. The holy city of Najaf smart transportation strategy seeks to reduce dangerous levels of traffic-related noise and air pollution while enhancing various aspects of mobility and traffic flow indicators. This study aims to evaluate the performance of the existing traffic network and public transport in Najaf City and its interference with noise and air pollution at selected points for data gathering by using field measurements using cameras, noise meters, and pollution measurement devices. The study states that the public transport sector in Al-Najaf city is significantly poor as private cars are dominant by about 65% of traffic mix with values of pollution and noise above the standards. One of the most effective solutions to traffic problems is the implementation of intelligent transportation systems. Part of these strategies is establishing a tram network and raising road classes’ strategies by proposing some geometric design editing, U-turns reducing, and raised ramps additions.

Implementasi Support Vector Machine Pada Alat Monitoring Kecelakaan Dengan Intelligent Transport System

The implementation of intelligent transportation systems will produce a large amount of data. The resulting data is critical in the design and implementation of ITS in the transportation system. This study discusses the performance of the Support Vector Machine algorithm on an accident monitoring tool by utilizing the Intelligent Transportation System that works in real-time using an Android-based application. This experiment simulates accident monitoring with a multisensor accident monitoring device. Multisensor technology consists of MPU 6050 sensor, sound sensor, vibration sensor, and camera. In an experiment, the measured variables are location, slope, accuracy, and time of the traffic accident monitoring system. The results of monitoring traffic accidents in testing using the Support Vector Machine algorithm can work well by classifying data based on the type of accident.

Proposed Policy to Manage the Barrier of the Implementation of Intelligent Transportation System

Proposed Policy to Manage the Barrier of the Implementation of Intelligent Transportation System

ОПЫТ ВНЕДРЕНИЯ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ В ГОРОДСКУЮ ИНФРАСТРУКТУРУ

The article discusses the implementation of intelligent transport systems on the street and road networks of megacities. The experience and reasons for the introduction of various intelligent transport systems in foreign countries are analyzed. The scheme of operation of existing systems is being studied.

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.

Forecasting public transit passenger demand: With neural networks using APC data

The implementation of Intelligent Transportation Systems (ITS) as a part of smart mobility is crucial for solving the current problems of the transportation industry. The setting up and maintenance of ITS requires not only the current passenger demand but also the future passenger demand. The future passenger demand can be obtained with time-series forecasting carried out with different techniques. With the advancements in the technological field, modern and more advanced methods of time-series forecasting using deep learning are being preferred over traditional forecasting techniques. However, the research carried out in this regard is quite limited, particularly considering the Indian scenario. Hence this research work focuses on exploring the performance of deep learning forecasting techniques considering the aspects mentioned previously. Here, the forecasting of passenger demand was done with Long Short-Term Memory (LSTM) using the three months Automatic Passenger Counter (APC) data of the Hubballi-Dharwad Bus Rapid Transit System (HDBRTS) as part of a case study. Then the forecasting of passenger demand was also done with Seasonal Autoregressive Integrated Moving Average (SARIMA), and the comparison of the forecasting accuracy of both methods was made using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Furthermore, to validate the results, novel approach has been adopted for the process, by following some more time-series resampled with different time intervals. Study shows that LSTMs will be used satisfactorily in the traffic conditions of developing counties, for forecasting passenger demand using APC data. Study also provides detailed guiding methodologies of advanced methods of passenger forecasting along with conventional ones.

Intelligent Transport Systems in Greece: Current status and future prospects

This paper reviews the development and implementation of Intelligent Transportation Systems (ITS) in Greece by adopting a methodology that uses Key Performance Indicators – KPIs. Based on these indicators ITS implementation in Greece appears to perform in varying ways depending on a number of factors that relate to the local conditions in each urban area. Based on the analysis and quantification of the KPIs selected, the analysis facilitates the identification of some areas on which ITS experts and policy makers should focus for further improvement and speeder implementation. The main problems that are indicated in the study, relate to the fact the large majority of ITS projects implemented in Greece are not adequately documented and do not provide, at least to a limited extent, information over the web regarding their status or their provided services. Another problem area relates to the fact that there are no ITS projects that go beyond the road sector to encompass multiple modes of transport and support intermodality and multimodality. Also, there is a weakness identified for more ITS implementations associated with freight transport and the concept of Mobility as a Service (MaaS).

ROLE OF INTELLIGENT TRANSPORTATION SOLUTIONS IN MULTIMODAL TRANSPORTATION SYSTEM

To ensure a successful implementation of Intelligent Transportation System (ITS) on the roadways, we must have an accurate assessment of the current condition of our transportation network and we need to continually monitor its performance and make necessary improvements as needed. To better understand how technologies like ITS are helping to optimise time on the road, it is good to gain a basic understanding of how this technology works. Intelligent transportation systems (ITS) are networks that offer users the option to either drive on a pre-programmed route that takes an average of around 40 to 45 minutes, or choose a different route that would take around 30 minutes. This often results in users spending less time in their cars when compared to a traditional drive-through model. A 2021 report from McKinsey & Company states “A typical smart city is envisioned, featuring shorter trips (either for commute, errands, or entertainment), along dedicated, on-demand routes to reduce the need to travel long distances as well as the physical inefficiencies of vehicle miles traveled". Integrated transportation services could also offer modern commutes a greater range of options, such as having the ability to purchase and use an electric vehicle (EV) onsite, or utilise public transit on the go. This multi-modal approach is proving more and more popular with businesses and cities, and many are making it a priority when it comes to implementing new infrastructure. Cities like Beijing, for example, have already started to incorporate autonomous vehicle (AV) fleets, which services drivers as they travel during their commute. Plans such as these portray a great future for the transportation network, but we have some way to go to reach this goal. Here we will discuss some other efficient solution for Intelligent Traffic Management.