Intelligent Transportation Systems: Theory and Practice

This chapter provides an overview of wired, wireless, and vehicular technology, information on intelligent transportation technologies, how wired, wireless, and vehicular technology has evolved, and how wireless communication works. In addition, this chapter briefly discusses wireless communication, the future of intelligent transportation systems and smart cities, a cooperative system on the road, and intelligent transportation. Aside from that, the chapter analyzes intelligent cities and related artificial intelligence techniques and contemporary concerns and challenges in wired, wireless, and vehicle technologies. It’s impossible to ignore the importance of transportation in today’s society. Onboard computers are becoming increasingly common in automobiles and trucks. Businesses and individuals desire cars that can connect to the Internet. Even though this should cover most of the country, technology is not universally available. In automobiles, sensors are used in more ways than ever before. Allowing movement between vehicles so that one terror can use the information provided by researchers to figure out where the cars are coming from can be beneficial. These are the most efficient methods for ensuring that people do not make any mistakes in the shortest time possible. In recent years, the automobile industry and academics have shown a strong interest in intelligent transportation systems, which improve road safety and traffic management. Because intelligent transportation systems use wireless communication, people must be concerned about security and privacy.

This chapter provides an overview of intelligent transportation systems and their history. Furthermore, the chapter discusses the present intelligent transportation system and its fundamental challenges in the Indian transportation system and remedies to the problems. Aside from the future of intelligent transportation systems, networked environments for intelligent mobility, intelligent transportation system applications, and early findings on intelligent transportation systems, the transportation industry is constantly changing, and it's critical for vehicles to keep improving as well. This is a critical transportation requirement not only for the here and now but also for our roads, vehicles, and users in the future. Current trends and applications of intelligent transportation systems (ITS) on automobiles and highways are discussed. Three of the most critical aspects of ITS are the intelligent vehicle initiative (IVI), commercial vehicle operations (CVO), and advanced rural transportation systems (ARTS). There are applications for commercial vehicles in the CVO category that make it easier for freight to move, carriers to manage their operations, and vehicles to be inspected. Advanced rural transportation systems aim to improve rural transportation, safety, efficiency, and communication. Accidents, slow traffic, and pollution are all on the rise for those in the transportation industry, and it's only getting worse. The intelligent transportation system can alleviate these challenges with the help of other infrastructures such as bridges and highways. ITS is currently progressing in the right direction for a full-scale implementation.

This chapter gives an overview of vehicle applications and related technology in the previous and next decade and related work on vehicle applications, such as 4G- vs 5G-based cars and next-generation-based future automobiles. Apart from that, the hyperloop transportation system and electric, hybrid, and linked automobiles are discussed in the chapter. Aside from that, the chapter covers vehicle data collection, future vehicle and intelligent transportation technology, and various vehicle types. The chapter also goes over the uses of cars linked by an intelligent transportation system. Based on real-time data about traffic flow on city roadways, an intelligent transportation system (ITS) provides consumers with sensible recommendations on getting around traffic and keeping the environment clean. However, as the use of traffic monitoring has grown, existing transportation systems that rely on cloud computing for storage, communication, and processing have been put under strain. As part of modern ITS, dynamic pricing aids in the cost-effective management of issues such as congestion control and peak load reduction. Dynamic pricing also helps traditional and electric vehicles move more efficiently. Optimizing car routes also contributes to creating a more environmentally friendly society. However, developing vibrant pricing schemes for ITS has always been difficult due to various constraints. Longer wait times, increased air and noise pollution, and waste of electricity and other resources could result from poor vehicle management. On the other hand, effective dynamic pricing techniques may please everyone, including service providers and users.

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.

The chapter describes the introduction of vehicle localization and navigation, related work done in-vehicle localization and navigation, and message passing in the Internet of things based on cloud vehicles. Furthermore, the chapter also covers vehicle navigation and localization, road detection and tracking in autonomous vehicles, and integrated Global Positioning System-enabled vehicles. This chapter also describes multiple sensors-based multiple object tracking, vehicle navigation and tracking on the Internet of things-based cloud vehicles, issues and challenges in-vehicle localization and navigation. Accurate and reliable location is significant for self-driving cars and other systems that help people drive: navigation and global place for intersection driving with a low-cost autonomous car with low-cost sensors in cities. To get a better idea of where the vehicle is, look at where it is about the lane and stop signs. First, the car learns where it is on the digital map and where the next lane and stop line are in the next intersection with the help of a Global Positioning System and odometer. A system that drives the car to its destination on its own, and researchers talked about it. Also, it does is use information from inertial sensors to make things move faster. A self-driving car can see where it is going by using radio detection and ranging, light detection and ranging, Global Positioning System, and computer vision, as well as other things. This system has a rotatable laser range finder that can see any obstacles.

Introduction to environmental sustainability for intelligent transportation systems, mobile element of an intelligent transportation system vehicle, types of intelligent transportation systems and ecological sustainability for intelligent transportation systems, techniques for environmental sustainability for intelligent transportation systems, and challenges in environmental sustainability for intelligent transportation systems are all covered in this chapter. Aside from that, the chapter also discusses the intelligent transportation system’s environmental management system and its future development. When there is a lot of traffic, bad driving habits, or bad weather, it can be difficult for people who use urban rail systems to get around. People in charge of city rail systems have been paying more attention to how well people can withstand and quickly recover from problems. A few recent studies have looked at how an urban rail system can recover from disruptions while considering its structures, but this isn’t the whole picture. Automobile manufacturers and researchers want to keep people safe on the road, reduce carbon dioxide emissions, use less fuel, improve driver comfort, find faster routes, and save money. If cars could communicate with each other and other objects, a significant improvement in safety and driving systems could be achieved. A “smart city” is a city model that views cities as complex systems that learn from their interactions and change over time. A model is a collection of things, such as services, resources, and citizens that work together in both space and time.

Introduction to fog, edge, and cloud computing, routing on the Internet of things-based cloud vehicles, intelligent vehicle tracking and navigation, and security-based architecture using cloud computing are all covered in this chapter. In addition, the chapter cover describes a secure, privacy-preserving architecture for future vehicles using fog computing and a safe, privacy-preserving architecture for future vehicles using edge computing. Aside from that, the chapter discusses current trends in intelligent transportation systems and future research opportunities for edge computing-based vehicles. However, with the advent of the Internet of things, these devices are now producing essential data. Fog computing, its taxonomy, how it differs from cloud computing and edge computing, its applications, new technologies, and some of the issues it raises are discussed in this chapter. Residents of intelligent cities can now navigate using data from their smartphones and other smart devices. Large amounts of data, securing cloud and vehicle networks and improving service reliability, efficiency, and usability are complex tasks. The use of IoT in intelligent transportation systems, both now and in the future. A dynamic ride-sharing dilemma with an edge/fog computing solution. A lot more things will be networked when the new 5G networks launch. Cloud computing can be used in networks where there are a lot of devices sending and receiving data but with no need for high latency or traffic congestion. The network can improve mobility, security, and privacy with edge computing.

Introduction to security, privacy, and trust in intelligent transportation systems (ITS), security, privacy, trust requirements in intelligent transportation systems, and cryptographic approaches in ITS are discussed in this chapter. In addition, the chapter discusses biometric-based intelligent transportation systems, blockchain-based intelligent transportation systems, and peer-to-peer ride-sharing via blockchain-based intelligent transportation systems. Aside from that, the chapter discusses the automotive intelligent transportation system and the various services available in an intelligent transportation system. Traditional cryptography is ineffective in a communication environment with limited space or time. A deep reinforcement learning algorithm is used to improve the performance of each subsystem as well as the overall mix of cars and trucks. Some argue that the blockchain is a public database that no one owns. A distributed data storage system with point-to-point transmissions and consensus mechanisms. Blockchain blocks store time stamps and digital signs in addition to using encryption algorithms, smart contracts, and other computer tools. Data is stored in multiple locations, making it easier to share data. The data records’ digital signatures can also verify the data. Because hash points connect the blocks, hackers cannot alter the data. People don’t have to trust a central authority or anyone else to agree on what’s going on with the ledger. Using the blockchain, people can share data securely, dependable, and traceable manner. There are several ways to improve transportation using blockchain technology. The blockchain has the potential to transform the concept of car sharing. All information is kept in one place on the blockchain, making it easy to find and track.

Introduction to intelligent transportation systems: need, operation, tools, intelligent transportation system (ITS) emergency vehicle scenario, and convolutional neural network model for intelligent transportation systems are covered in this chapter. In addition, the chapter discusses the need for intelligent transportation systems, how they work, and the critical stages of intelligent transportation systems. Aside from that, the chapter discusses intelligent transportation system patterns. This work discusses critical challenges in implementing an intelligent transportation system. This chapter discusses intelligent user services in an intelligent transportation system, and also discusses required architecture of vehicular networks/ITS. Big data and new technologies that make it easier and cheaper to collect, store, analyze, use, and share data from various sources have made this more accessible and cheaper. Because of the connected environment, new ways to control and manage transportation systems in real time are also emerging. These new methods of controlling and managing transportation systems will aid in the improvement of overall system performance. These systems use real-time data about traffic flow on city roads to assist people in avoiding traffic and maintaining a clean environment. There has been a significant increase in traffic monitoring, putting traditional transportation systems that rely on cloud computing under a lot of strain. In the last few years, the intelligent transportation system (ITS) has seen a lot of changes. Make trips more efficient: Many ITS technologies can assist people in reducing the number of unnecessary trips and increasing the number of trips taken by other modes. They can also help to reduce traffic congestion, reduce the need for foreign oil, and improve air quality.

Every year, we “humans” travel about fifteen thousand kilometers on average. That counts to about 100 trillion kilometers all of us together. A number that has 14 zeroes (“00,000,000,000,000”) trailing it. Obviously, it should be one of our main concerns of betterment. Effortless transport was always the pinnacle of our imagination. Vehicular ad hoc network (VANET) has made movement of travel from one place to another. With the rapid enhancement in VANET technology, autonomous vehicles have been come with efficient services (in today’s smart era). In current scenario, Internet of things (IoT) devices are being used for making automation and machine learning are used to make intelligent, i.e., to improve learning techniques. Now in the next step, we see in accomplishing that goal is intelligent transportation using autonomous vehicles (provide reliable and comfortable service to its users). For that to be true, we undoubtedly need an enormous number of sensors and software (including skilled people) to make as reality. Hence, this chapter discusses the depth we have reached in the endeavor and the stretches we must go to yet, with respect to AI-IoT-based intelligent transportation system (ITS).

Today with the Internet of Things (IoTs) we can easily connect real-world objects to the virtual world almost anytime and everywhere. It refers to a world in which real-world objects connect with virtual data. IoT-based solutions are playing a significant role in propelling the worldwide IoT in intelligent transportation systems (ITS). Communication among different automobiles via IoT would lead to a new era of connectivity that would lead to ITS. As more automobiles connect to the Internet, a tremendous volume of data is created. As a result, in order to construct effective systems, this massive volume of data must be managed and turned into meaningful information data. In this chapter, we focus on integrating intelligent transportation systems with the IoTs and Traffic optimization in intelligent transportation systems and their future prospect along with how they can solve real-world problems. Toward the end of the chapter, we have discussed open issues and their future.

Internet of Things (IoT) has created new opportunities for more efficient and safer travel today. IoT infrastructure, as well as its computational capabilities, can constitute what researchers call an Intelligent Transportation System (ITS). This chapter aims to explain the implementation of an ITS in a computing environment powered by IoT. Such a system connects IoT devices on the ground and various infrastructures, to a cloud that can perform complex data analytics. We look at the methods in which we can apply ITS technology to improve public transportation, control vehicles in more advanced ways, and make our streets safer. ITS opens the door to an enormous amount of benefits. Data sensed on the road can provide us insights into traffic, travel, and business. IoT devices can be crucial in providing much-needed information in the case of a medical emergency. These different functionalities together integrate to form an intelligent system capable of integrating itself into the current infrastructure without disrupting our everyday lives. In this chapter, we have discussed how ITS can improve transportation in many sectors that rely on the travel of goods and people, such as agriculture, supply chain, and defense. We have also discussed how a city can plan an ITS to convert itself into a smart city.

With the advancement of urbanization and the ongoing construction of transportation infrastructure, traffic congestion has become a major issue in our lives. The government’s main concern in implementing social management is how to assure traffic safety. The Internet of Things (IoTs) is now widely employed in the realm of industrial technology. It will have a significant influence on human productivity and quality of life. Intelligent transportation systems (ITS) are a topic of study that encompasses a wide range of advanced and novel technology. This chapter talks about vehicular and cooperative cloud computing. It also talks about the role of big data and challenges and issues faced by the use of big data in IoT-based ITS. Moreover, we have discussed about smart cities and smart transport using IoT-based cloud applications. Toward the end, we have added future research opportunities.

A safe and sustainable intelligent transportation and human behavior system can reliably extract knowledge from traffic data in cities. The data is safeguarded utilizing blockchain and learning techniques, with a threaded Graphics Processing Unit (GPU) for scalability. Different improvements are also available to efficiently process information on the GPU. To combine local information found on every site into global knowledge, a reinforced profound trained model is also built. Internet of Things (IoTs) technologies play a critical role in global transportation. Communication between automobiles via IoT will usher in a new era of communication that will lead to intelligent transportation systems (ITS). ITS technology is emerging as a worldwide revolution in recent years, having a significant influence on the transport and automation industries. As a result, academics’ focus has shifted to the utility of ITS technology, motivating us to give a complete study. As a result, this study brings together current research as well as some potential uses of intelligent transportation system technology in transportation. It traces the growth of ITS studies, also known as transportation models, since their inception and tries to provide a comprehensive analysis of various ITS technologies, including their strengths and drawbacks. Energy management is viewed as a cost-effective, creative solution to high-efficiency power plants. It optimizes conventional sources of the IoT-based intelligent transport system while also assisting in the automation of railroads, highways, airways, and shipways, hence improving customer experience. ITS is being developed to provide informed judgment and cooperative sensing in order to address the growing need for better autonomous transportation. However, in the dynamic era of current apps and the inflexible architecture of legacy Internet, software-defined transportation infrastructure must be adaptable, inventive, adaptive, and programmable (SD-ITS). The logically centralized intelligence of SD-ITS might be a key target of current cyber-threats and assaults, causing the entire network to go down in flames.

Intelligent Vehicles (IVs) have become a priority for research because of their significant benefits in enhancing road safety and operational efficiency. Intelligent Vehicles have made enormous progress over the years, but they still face substantial obstacles in gaining widespread acceptance. The authors recommend, in this regard, that Intelligent Vehicles research be advanced anywhere, gradually addressing existing issues. To begin with, the most basic necessity of Intelligent Vehicles is security. The authors outline the essential technologies and problems of Intelligent Vehicles’. It includes fundamental architecture, existing attacks, and defense tactics for information security. Second, comfort is primarily concerned with people’s subjective sensations. Visual perception is the primary, which humans integrate data from the traffic environment. Visual sensors can gather the majority of the information needed to operate a vehicle. Necessitating advanced machine vision and picture interpretation approaches for visual sensing. The functional spectrum covered ranges from sophisticated automated driving to self-driving vehicles. This chapter follows the order of image processing pipelines, which consolidate the rich information and a large quantity of data in video sequences one by one. Specific items in a traffic scene are associated using recognition and classification systems. Although several Intelligent Vehicle prototypes have been constructed to demonstrate the notion of automated vehicles and the viability of enhancing traffic efficiency, there is still a considerable gap until high-level IVs can be mass-produced. The goal of this research is to provide an overview of present technologies. It will be practical in future Intelligent Vehicles, their current condition, and prospects. Reviewing all linked works and predicting their future perspectives is a strenuous endeavor, especially in such a vast and multi-disciplinary field of study.

This chapter is written exclusively for researchers and industry professionals, who can look or predict many useful benefits of intelligent transportation system (ITS) in many sectors.

This book has been written to learn more about vehicular ad hoc networks [1], intelligent transportation system, and its related emerging technologies for next-generation society/industry. Toward this, we have discussed many topics in each chapter with a different application of transportation/vehicular network in this book. Hence, now the summary of each chapter can be explained as.