# Optimal Routing in Urban Road Networks: A Graph-Based Approach Using [PERSON]'s Algorithm
## 2 Literature Review
The focus of the research in the proposed paper is the development of the main indicators for each of the above-mentioned goals. Taking into account the aforementioned global goals, it is concluded that the topic of this paper and the shortest path algorithm represent the correct approach for developing indicators to monitor progress in transport sustainability, which is also the goal of the aforementioned study. The 2030 Agenda for Sustainable Development foresees the development of the planet through 169 goals. Some of those goals relate to building resilient infrastructure for cities and countries, promoting inclusive and sustainable industrialization, and encouraging innovation [26]. One of the goals is to transport passengers and goods as quickly as possible, and this is achieved by improving scientific research and upgrading technological capabilities. In response to the aforementioned goal of this agenda, the task of this paper is to find the shortest route as efficiently as possible to carry out the transfer of goods and passengers through the city core. In the 2014 publication \"Poverty and sustainable transport\", authors [PERSON] and [PERSON] report that urban growth and densification are increasing travel distances. This causes more complex journeys and makes it difficult for public transport to run in and out of city centers. Despite significant road construction, congestion is worsening, and average traffic speeds are falling. Crowds affect all road users. Congestion solutions include finding the shortest road route, including the construction of large-capacity city roads. Shorter travel times will also reduce the impact of exhaust gas emissions, which is another benefit of finding the shortest routes [27]. In light of achieving better development of the city traffic network, there is an increasing effort to improve the vehicle management system in order to achieve better and higher quality driving conditions [28]. [PERSON] et al. in their work propose the \"adaptive cruise control strategy for intelligent vehicles based on hierarchical control\". The higher-level controller calculates the desired vehicle acceleration based on model predictive control and switches between speed control and gap control according to driving conditions. A joint simulation platform is established using PreScan 8.5.0, CarSim 2019.1, and MATLAB/Simulink R2021a. The proposed control strategy can accurately and safely follow the target vehicle in various driving conditions and meets the requirements of fuel efficiency and comfort, thereby reducing travel time. Robots are increasingly used in motion planning, obstacle-free path navigation, and finding the shortest paths with dynamic collision avoidance. These approaches are based on the use of machine learning [29]. For this purpose, the existing simplex architecture for ensuring safety in critical systems has been extended with an adaptation mechanism, in which one of the redundant controllers (called the high-performance controller) is represented by a trained machine learning model. Evaluation results show that the adaptive simplex architecture (ASA) provides responses for the robot when encountering obstacles in the real world. If the pathfinding conditions are not demanding, the solution predicted by the model is economical in terms of path length. The development of urban traffic networks is increasingly being followed by the development of autonomous vehicles. In this regard, traffic route planning within the city uses solutions for perception, localization, and mapping to suggest the best, i.e., the shortest and fastest, route to the vehicle in order to reach the desired destination with the help of artificial intelligence. After selecting the route, the control system sends the necessary values, such as acceleration, steering elements, and steering angle, to successfully navigate the vehicle along the chosen route.