With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure.

With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the laws and privacy regarding the use of drones in the urban planning context play an essential role in future smart cities. This study provides a comprehensive survey of the regulation of drone application and drone management systems, including a comparison of existing rules, management methods, and guidelines in drone operation to guarantee the safety and security of people, property, and environment. Evaluating existing regulations and laws practiced worldwide will assist in designing drone management and regulation. In Vietnam, the current rules can manage and regulate general guidelines of drone operations based on prohibited, restricted, and controlled areas within the urban context. The legislation, however, is unclear as to how it regulates smaller civilian unmanned aircraft used in the country. In the legal aspect, the potential consequences consist of the inefficiency of compensation responsibility, the violation of drone regulations, and information insecurity.

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.

Drones are estimated to play a critical role in the smart city, assisting with a variety of use cases: medical, transportation and agriculture. The applications of drones in the smart city will involve multiple drone platforms that operate simultaneously to run missions. Therefore, the safe and secure environment for drones' operational quality and stability is necessary. It is also essential for governments to implement regulations to enforce safe security standards and disallow the implementation of weak cybersecurity measures in live environments. The Federal Aviation Administration (FAA) predicted that 30,000 drones could be flying in U.S. skies in less than 20 years. The investigation of the drone traffic safety and development of the intelligent transportation system needs drone-following models, which describes the one-by-one following process of drones in the traffic flow. There are two types of drone-following models that are proposed and discussed in this paper. The first models based on the principle that keeps a safe distance according to relative velocity, which based on the determining of the drone acceleration depending on the differences in speeds and gaps between the given drone and its leading drone. Another model is the Markov drone-following model which is improved model based on the approximation of the stochastic diffusion process of speed decision. The simulation results show that the changes in velocities of the following drones are nearly the same as leading one.