# Optimal Routing in Urban Road Networks: A Graph-Based Approach Using [PERSON]'s Algorithm
## 2 Literature Review
Additionally, there is knowledge about establishing connections between vehicles and infrastructure to create an environment where the vehicle can predict potential obstacles and events that it will encounter along the way. In this way, artificial intelligence is an important tool in determining traffic flow density. In their 2020 study, [PERSON] and others examine studies that use deep learning sensor fusion algorithms for perception, localization, mapping, and determining obstacles, shortest paths, and environmental characteristics [30]. The loss of time at traffic lights is an important factor in the formation of traffic flow in a city. The problem of managing the operation of signalized intersections is a very complex process that consists of a large number of factors, such as primary criteria group (vehicle time losses, total number of stops, queue length, etc. ), safety criteria, economic and environmental criteria, and user criteria. In this regard, a large number of authors have worked on managing the operation of signalized intersections with the goal of improving traffic flow conditions from a functional, economic, and environmental perspective, which is very important in the final planning of the urban traffic network. One of the first developed models is the Webster model, based on which the cycle calculation and the distribution of green times for the relation are performed at an isolated signalized intersection. Later models developed by [PERSON] and his collaborators had shortcomings, because they could only be used in unsaturated traffic flows, and the model also considered an isolated intersection, assuming that vehicle arrivals are random (not applicable for cases with low or high fluctuation levels in vehicle arrivals) [31]. [PERSON] et al. pointed out the impact of saturated flow on time losses, queue length at intersections, and other parameters of signalized intersections [32]. The authors [PERSON] and [PERSON], followed by [PERSON] and later [PERSON], use the saturated flow of a traffic lane as a basis for calculating signal durations, intersection capacity, and the degree of saturation. In their studies, they assume that the saturated flow remains constant throughout the duration of the green signal phase. However, research has shown that the saturated flow gradually increases several seconds after the appearance of the green signal phase, then remains approximately constant for 56 to 60 s of green light duration, after which it begins to decrease until the end of the green light duration. Based on these results, the authors suggest that the maximum duration of the green light within a single cycle should not exceed 45 s, while the minimum duration is proposed to be 20 s. Outside this interval, the real values of time losses significantly deviate from the corresponding values obtained under the assumption that the saturated flow is constant throughout the entire duration of the green light in the cycle. For the maximum duration of all phases within a single cycle, a period of 120 s is proposed [33]. The literature review presents previously published works for analyzing the shortest path in urban transportation networks. Most of the algorithms listed are evaluated in terms of distance, without a comprehensive consideration of factors that affect the functionality of urban traffic flow, meaning there is still a significant gap in this research area that needs to be filled with future studies. Therefore, this paper proposes a new model for determining the shortest path with sparsely placed counters by applying [PERSON]'s algorithm, considering the following functional characteristics of traffic flow: average traffic flow speed, time loss at signalized intersections, and one-way streets. The new algorithm is implemented in the AutoLISP programming language, integrated with AutoCAD, and together, they form a significant tool and practical application for traffic engineers, civil engineers, and planners working in this environment.