Recent developments in urban freight analytics for collaborative city logistics

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.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)

A multi-criteria decision model for stakeholder management in city logistics

Emerging technologies, like cloud computing, artificial intelligence (AI), and internet of things (IOT), are advancing the evolution of intelligent transportation systems (ITS). For successful technology integration, researches on ITS engineering often apply theories and tools of system engineering, among which system readiness level (SRL) is a fitting tool for system development. This paper built a model based on SRL analysis for highway ITS engineering. Further, the author analyzed the spatiotemporal features of highway ITS, including distributed computing resources and network capabilities, unbalanced development of existing information and communication technologies (ICT), and inherited architectural limitations of existing ITS infrastructures. The analysis indicated the need for economic consideration on highway ITS engineering. Therefore, this paper improved the SRL-based engineering model with a value engineering process. The effectiveness of the model was verified by the deployment of 5.8GHZ ambiguity path recognition technology, from aspects of system development and economic efficiency.

The vision of future mobility has been reshaped continuously since introducing the first cars widely available to the public in the early 20th century. Technological breakthroughs, like the introduction of electrical drivetrains, autonomous vehicles, and more comprehensively Intelligent Transport Systems (ITS), can be seen as a process of evolution, not just spontaneous revolutions. Like in other industry sectors, state-of-the-art Information and Communication Technology (ICT) plays a significant role. It includes Artificial Intelligence, the Internet of Things, Vehicle Ad Hoc Networking, Big Data, etc. In addition to that, there are other important factors, like the impact of transport systems on the ecosystem, i.e. environment around us. Finally, the users of the systems, all of us, local and global communities, have their say in developing mobility as a Service (MaaS). This paper represents a survey report on public opinion in Saudi Arabia on future mobility changes in the transportation system.

This article is single-source introduction to the emerging concept of smart cities. It can be used for familiarizing researchers with the vast scope of research possible in this application domain. The smart is primarily concept, and there is still not clear and consistent definition among practitioners and academia. As simplistic explanation, smart is place where traditional networks and services are made more flexible, efficient, and sustainable with the use of information, digital, and telecommunication technologies to improve the city's operations for the benefit of its inhabitants. Smart cities are greener, safer, faster, and friendlier. The different components of smart include smart infrastructure, smart transportation, smart energy, smart health care, and smart technology. These components are what make the cities smart and efficient. Information and communication technology (ICT) are enabling keys for transforming traditional cities into smart cities. Two closely related emerging technology frameworks, the Internet of Things (IoT) and big data (BD), make smart cities efficient and responsive. The technology has matured enough to allow smart cities to emerge. However, there is much needed in terms of physical infrastructure, smart city, the digital technologies translate into better public services for inhabitants and better use of resources while reducing environmental impacts. One of the formal definitions of the smart is the following: connecting the physical infrastructure, the information-technology infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city [4]. Another formal and comprehensive definition is a smart sustainable is an innovative that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operations and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects [12]. A broad overview of various components needed in smart is depicted in Figure 1. Any combination of various smart components can make cities smart. A need not have all the components to be labeled as smart. The number of smart components depends on the cost and available technology.

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.KeywordsIntelligent transportation system (ITS)Information and communications technologies (ICTs)(PDAs)(DSRC)Internet of Things (IoT)

Developing a micro-simulation tool for autonomous connected vehicle platoons used in city logistics

At present, Intelligent Transportation Systems (ITS) are fast developing and have been applied in many fields. The Internet of Things (IOT) provides a new direction for their development. This paper first introduces the concept and structure of the IOT and states the high integration between the IOT and ITS. At the same time, it presents the frame of the ITS based on the IOT and describes the system function. Then, the paper elucidates the application of an intelligent sensor, radio frequency identification (RFID), positioning technology and a geographic information system (GIS) in ITS from three stages: data collection, data transmission and information processing. The paper discusses the differences between the new system frame and the existing system frame. Finally, the paper presents the application prospects of ITS based on IOT.

Modeling

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

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.

Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.

<p>Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network.</p> <p>Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the [..] traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation.</p>

As the rapid advancement and usage of Artificial Intelligence (AI) and the Internet of Things (IoT) have significantly increased the modernization in the autonomous vehicles, converting them from prototypes that are experimental, to a practical solution for next/new generation transportation systems. AI techniques like computer vision, deep learning, and reinforcement learning empower vehicles to perceive their surroundings, make intelligent driving decisions, and adapt to dynamic or extreme road conditions. Wherever IoT ensures flawless communication through Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and cloud-based networks, enabling the exchange of critical data in real-time. Using these technologies together enhances or improves road safety for individuals, also helps to reduce clogging, maximizes fuel efficiency, and promotes sustainable mobility in urban areas. Moreover, AI and IoT allow/enable the vehicles to support innovations like precision maintenance, intelligent traffic coordination, and intelligent fleet management. Along with these advancements, there are challenges such as the higher cost for advanced technology, cybersecurity risks, governmental policies, and the acceptance of the public, which is very critical to address. The integration of the IoT and AI in upcoming autonomous vehicles showcases a step towards the intelligent transportation system, having the capacity to redefine terms such as mobility, logistics, and planning the urban areas in upcoming decades.