The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life.

Signal strength degradation represents a variation in the coverage area for radio networks. Building maps that represent this degradation requires collecting information about signal coverage in scattered locations, which can be done conventionally by measurement methods such as the manual drive test. Nevertheless, as this process is large-scale, time-consuming, and costly, several methods for the minimization of drive tests have been introduced. In this study, our methodology first consisted of dividing the study area into several zones, and each zone into several sectors without considering the position of the existing broadcast base station. Then, we developed a custom mobile application to collect the signal strength data and the location coordinates of the concerned area. For data collection, we deployed the mobile application on more than 10 users' phones, who navigated in different areas using their cars. We applied the gradient-boosted trees algorithm to predict signal strength degradation in different areas. Our model has shown some interesting results after studying and analyzing the collected data, based on data mining algorithms. We also evaluated our model's ability to predict the zone's structure according to the strength of the degradation signal.