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The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

The purpose of this research work is to design and apply LabVIEW in the area of traffic maintenance and flow, by introducing improvements in the smart city. The objective is to introduce the automated human–machine interface (HMI) – a computer-based graphical user interface (GUI) – for measuring the traffic flow and detecting faults in poles.

This research paper is based on the use of LabVIEW for designing the HMI for a traffic system in a smart city. This includes considerable measures that are: smart flow of traffic, violation detection on the signal, fault measurement in the traffic pole, locking down of cars for emergency and measuring parameters inside the cars.

In this paper, the GUIs and the required circuitry for making improvements in the infrastructure of traffic systems have been designed and proposed, with their respective required hardware. Several measured conditions have been discussed in detail.

PJRC Teensy 3.1 has been used because it contains enough general-purpose input–output (GPIO) pins required for monitoring parameters that are used for maintaining the necessary flow of traffic and monitor the proposed study case. A combination of sensors such as infrared, accelerometer, magnetic compass, temperature sensor, current sensors, ultrasonic sensor, fingerprint readers, etc. are used to create a monitoring environment for the application. Using Teensy and LabVIEW, the system costs less and is effective in terms of performance.

The microprocessor board shields for placing actuators and sensors and for attaching the input/output (I/O) to the LED indicators and display have been designed. A circuitry for scaling voltage, i.e. making sensor readings to read limits, has been designed. A combination of certain sensors, at different signals, will lead to a secure and more durable control of traffic. The proposed applications with its hardware and software cost less, are effective and can be easily used for making the city’s traffic services smart. For alarm levels, the desired alarm level can be set from the front panel for certain conditions from the monitoring station. For this, virtual channels can be created for allowing the operator to set any random value for limits. If the sensor value crosses the alarm value, then the corresponding alarm displays an alert. The system works by using efficient decision-making techniques and stores the data along with the corresponding time of operation, for future decisions.

This study is an advanced research of its category because it combines the field of electrical engineering, computer science and traffic systems by using LabVIEW.

In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects.

The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic.

In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision.

The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.

The purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities.

This research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment.

The outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India.

The paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.

The purpose of this paper is to use the conceptual model of the Smart Transportation Management (STM) system and analyze how the included factors change the performance of distribution activities and what management issues are at stake.

To prepare the paper, a literature study was made and case studies carried out in companies and organizations that are included in advanced transportation setups, including infrastructure providers, carriers, truck manufacturers, software providers, shippers, and more.

The main finding of the study is a model that includes three major components of smart transportation management, namely, smart goods, smart vehicles and smart infrastructure. These components embrace some factors that have effects on supply chain performance; however, to different extents.

The paper uses a framework for the smart transportation management system that is useful when studying advanced transportation management systems, the functions that need to be supported and what factors have effects on supply chain performance.

Practical implications are mainly based on the structure of the smart transportation system that is used and the identified factors that affect the performance of the supply chain, as these factors can be influenced by logistics management.

The framework used in this research is a new development that collects advanced functions of goods identification, vehicle information systems and infrastructure systems into one conceptual model for smart transportation management that include some factors that are affecting supply chain performance.

The purpose of this paper is to analyze the impact of smart city initiatives and big data on supply chain management (SCM). More specifically, the connections between smart cities, big data and supply network characteristics (supply network structure and governance mechanisms) are investigated.

An integrative framework is proposed, grounded on a literature review on smart cities, big data and supply networks. Then, the relationships between these constructs are analyzed, using the proposed integrative framework.

Smart cities have different implications to network structure (complexity, density and centralization) and governance mechanisms (formal vs informal). Moreover, this work highlights and discusses the future research directions relating to smart cities and SCM.

The relationships between smart cities, big data and supply networks cannot be described simply by using a linear, cause-and-effect framework. Accordingly, an integrative framework that can be used in future empirical studies to analyze smart cities and big data implications on SCM has been proposed.

Smart cities and big data alone have limited capacity of improving SCM processes, but combined they can support improvement initiatives. Nevertheless, smart cities and big data can also suppose some novel obstacles to effective SCM.

Several studies have analyzed information technology innovation adoption in supply chains, but, to the best of our knowledge, no study has focused on smart cities.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

This chapter critically evaluates the opportunities and challenges in adopting information technology for enhancing transportation in developing countries. The conception framework emerging from this study highlights four key stakeholders and the country context in developing and integrating information technology for improving transportation in developing countries – Tech developers, Transporters, Transport Tech start-ups and Travellers. This study makes cogent theoretical contributions to the growing body of works around technological innovation, smart mobility and intelligent transport systems, albeit from a developing country perspective. This study has also emerged from managerial implications for stakeholders, especially the tech developers, transporters and transport tech start-ups. There is a need for innovative ideas to address the inherent transportation challenges in Africa, and the customers are counting on these stakeholders to make the technology readily available and accessible.

The purpose of this paper is to address a project for lighting an old town in Italy. Its originality lies in the holistic approach that aims to fulfil several objectives. One is to reduce energy consumption by using efficient lamps and advanced control systems; the second one is to make the network viable and useful for many purposes by integrating ICT devices; the third one is to provide a new identity to the older part of the city by using new technologies and design concepts; while the last one is to ensure street and pedestrian safety according to codes and standards.

The plan of the city of Bagheria and the stock of luminaires of the city are analysed. A multidisciplinary approach has been adopted in order to: analyse the existing lighting infrastructure highlighting critical areas; design a new displacement and select typologies of luminaries able to provide proper light quality and distribution; propose an aesthetic solution and technical design for relevant historical building; and to include in the design process the concept of a new multifunctional pole. Together with an analysis of social benefits, an assessment of economic costs and benefits are discussed.

The project allows good energy savings, meets the standard requirements and gives a relevant and strategic improvement in social and environmental management of the city.

The work provides an example of integrated design of street lighting infrastructures for urban renovation in old cities in degraded environments.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.