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Transport engineering has recently undergone several significant changes and innovations, one of which is the appearance and spread of autonomous vehicles; with this technology becoming more common and ordinary by the day, it is now necessary to implement some systems and contexts to facilitate autonomous vehicle operations. Consequently, a different perspective is now arising when dealing with road infrastructures, aiming to simplify and improve efficiency and maintenance of the existing roads, increase the life cycle of newly built ones, and minimize the economic and financial impact at the same time. Roadway pavements are one of the primary factors affecting vehicle operations; over time, this distinctive aspect has gone through various mechanical and physical changes due to the adoption of new materials or design methods. Consequently, to the spread of autonomous vehicles, scientific research has begun to study and develop systems to make road pavements and platforms not exclusively aimed at bearing loads, but rather at considering them as a means of communication and information exchange, if not even as a source of energy. This new approach introduces the so-called “Smart Roads,” i.e., road infrastructures capable of communicating with vehicles and self-monitoring fundamental perspectives concerning driverless vehicles and the roadway platform life cycle. This paper examines the characteristics of Smart Roads, considering their broad field of application and their potential advantages and drawbacks. This paper also pursues the objective of describing the global vision, the possible future direction of these innovations concerning the automotive and transport industries, and a particular focus on infrastructures and roadways.

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems.

Pavement vibration monitoring under vehicle loads can be used to acquire traffic information and assess the health of pavement structures, which contributes to smart road construction. However, the effectiveness of monitoring is closely related to sensor performance. In order to select the suitable acceleration sensor for pavement vibration monitoring, a printed circuit board (PCB) with three MEMS (micro-electromechanical) accelerometer chips (VS1002, MS9001, and ADXL355) is developed in this paper, and the circuit design and software development of the PCB are completed. The experimental design and comparative testing of the sensing performance of the three MEMS accelerometer chips, in terms of sensitivity, linearity, noise, resolution, frequency response, and temperature drift, were conducted. The results show that the dynamic and static calibration methods of the sensitivity test had similar results. The influence of gravitational acceleration should be considered when selecting the range of the accelerometer to avoid the phenomenon of over-range. The VS1002 has the highest sensitivity and resolution under 3.3 V standard voltage supply, as well as the best overall performance. The ADXL355 is virtually temperature-independent in the temperature range from −20 °C to 60 °C, while the voltage reference values output by the VS1002 and MS9001 vary linearly with temperature. This research contributes to the development of acceleration sensors with high precision and long life for pavement vibration monitoring.

In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm.

Air pollutant monitoring is a basic issue in contemporary urban life. This paper describes an approach based on the diffused use of low-cost sensors that can be mounted on board urban vehicles for more abundant and distributed measures. The system exchanges data, exploiting a “Smart Road” infrastructure, with a central computing facility, the CIPCast platform, a GIS-based Decision Support System designed to perform real-time monitoring and interpolation of data with the aim of possibly issuing alarms with respect to different town areas. Experimental data gathering in the Rome urban area and subsequent processing results are presented. Algorithms for data fusion among different simulated monitoring systems and interpolation of data for a geographically denser map were utilised. Thus, in the framework of the Smart Road, protocols for data exchange were designed. Finally, air pollutant distribution maps were produced and integrated into the CIPCast platform. The feasibility of a full system architecture from the sensors to the real-time pollutant maps is shown.

Aimed at the “smart individual” of the road, the functional and performance requirements in the process of Planning, Construction, and Maintenance for the whole life cycle of Smart Road is analyzed. Next, BIM, GIS, IoT, Big Data and AI and other advanced information technologies are integrating these technologies into the life-cycle management of smart road in this paper. Therefore, the “three-layer” logical architecture model is conducted. Then, according to the knowledge of Bionics, Smart Road is compared to a smart individual. The critical technologies needed for the SR life cycle system are analyzed based on the key elements required for the growth and development of intelligent individuals. Finally, the implementation scheme and process are analyzed in detail, which provides a reference for future SRs researchers.

The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.

As the importance of safety, efficiency, and sustainability in urban transportation becomes more apparent, intelligent transportation systems are changing and growing. Smart intersections play a crucial role in different parts of this context. Technologies such as Vehicle-to-Everything (V2X) communication, artificial intelligence, multi-sensor data fusion, and more are incorporated into these intersections to improve capacity and safety and reduce damage to the environment. This literature review aims to merge various recent works on advancing smart intersection technologies, their thematic application, methodological approach, and regional implementations. Highlighting adaptive traffic signal control, real-time data processing, and connected autonomous vehicle (CAV) integrations sheds light on the way the effectiveness of transportation in cities can be improved. At the same time, this study tackles questions of cybersecurity and standardization. This review provides insights for researchers, policymakers, and practitioners who aim to improve transportation systems’ sustainability, fairness, and operability.

Smart pavement is composed of a monitor network, communication network, data center, and energy supply system, and it requires reliable and efficient energy sources to power sensors and devices. The mechanical energy is wasted and dissipated as heat in traditional pavement; this energy can be reused to power low-power devices and sensors for smart pavement. Mechanical energy harvesting systems typically perform through electromagnetic, piezoelectric, and triboelectric methods. Among the different methods, electromagnetic harvesters stand out for their higher output power. However, current electromagnetic harvesters face challenges such as bulky designs, low power density, and high input displacement requirements. This study proposed a green electromagnetic harvester (GEH) based on up-frequency and a unidirectional rotation mechanism to harvest mechanical energy from the pavement. A prototype was designed and prepared. The influence of different parameters on the electrical performance of the harvester was studied by using an MTS test instrument and simulation methods. The results demonstrate that increasing the frequency and optimizing the magnetic array significantly enhances electrical output. The open-circuit voltage in the N-S mode is 3.1 times higher than that in the N-N mode. At a frequency of 9 Hz and a displacement of 3.0 mm, the open-circuit voltage of the GEH is 6.73 V, the maximum power output is 171.14 mW, the peak power density is 1277.16 W/m3, and the voltage has almost no decay after 100,000 cycles. Further, the application of the GEH in charging sensors and capacitors was demonstrated, which indicates the potential of a GEH to power sensors for smart roads.

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.