The Importance of Google Maps for Traffic in Calculating the Level of Service for the Road and Traffic Delay

Abstract

Traffic congestion is one of most important problems generated in the modern era, due to the need to move quickly between different sites, technological development, economic and population growth. Led to a significant increase in private transportation vehicles in urban areas. It has even exceeded the capacity of the planned road in some locations, especially those that attract trips significantly, causes cluster of vehicles, traffic congestion, and delay in the trip, which causes material and psychological costs to people. Therefore, this problem needs to be defined accurately and quickly in order to address it. There are many methods, including the traditional and advanced ones, in determining congestion and the amount of delay. Since determining the amount of delay for many sites is very expensive and takes a long time. We assume there is a method to facilitate data acquisition and delay in a fast and cost-effective manner. Through the use of websites on the Internet, one of which is Google traffic maps. Works to provide data on the traffic situation of the roads in any area, and at any time in the world, which can be used to know the amount of delay of the trip in this area. As it was noted that there is a failure in previous studies to convert this data into a data that can be used. Conversion descriptive data to numerical, for processing an accurate measurement.

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

“…Travel time prediction consists of navigation and route construction, which is very important for transportation networks, as web mapping services and services regularly serve a large number of travel time requests from users and businesses. At the same time, there is a need to account for complex spatio-temporal interactions, which is explained by the topological properties of the road network, where traffic jams can be expected during peak hours and as a result of road accidents [5,12].…”

“…In parallel with this, AUV with the help of MLM is able to recognize people, road signs, traffic light indications and route instructions, which is explained by machine learning of the network, where neural networks of deep learning can be used among such examples [1,4]. However, in order to solve the problems of processing with pattern recognition on the road, it is necessary to take into account unforeseen factors that can be avoided with the help of navigation systems with the construction and optimization of an appropriate route, which allows to reduce the time spent on the road [5]. TECHNOLOGY AUDIT AND PRODUCTION RESERVES -№ 1/2(75), 2024…”

“…Deep learning is one of the areas that has been significantly developed in the last ten years, and the possibilities in the application of this technology allow a wide range of applications in robotics with automated processes. Therefore, deep learning pushed the boundaries of what was possible in the field of digital image processing [5].…”