Energy-optimizing machine learning-driven smart traffic control system for urban mobility and the implications for insurance and risk management

2025-02-27T01:23:18+00:00

Heavy traffic during peak hours, such as early mornings and late evenings, is a significant cause of delays for commuters. To address this issue, the prototype of a dual smart traffic light control system is constructed, capable of dynamically adjusting traffic signal duration based on real-time vehicle density at intersections, as well as the brightness of the streetlights. The system uses a pre-trained Haar Cascade machine learning classifier model to detect and count vehicles through a live video feed. Detected cars are highlighted with red squares, and their count is extracted. The vehicle data is then transmitted to an Arduino microcontroller via serial communication, facilitated by the pySerial library. The Arduino processes this information and adjusts the timing of the traffic lights accordingly, optimizing traffic flow based on current road conditions. A novel approach involves optimizing energy usage through real-time data integration with the power grid. Street lighting is then dynamically adjusted at night times—brightening during high-traffic periods and dimming during low-traffic times. The brightness levels are set at 30%, 50%, 75%, and 100% based on the number of cars detected, with above 50% indicating the presence of cars. This adaptive control enhances energy efficiency by reducing energy consumption while maintaining road safety. The simulated and experimental results are provided. The former demonstrated a lower accuracy compared to the latter, particularly during the transition to the green light, across all traffic density levels. Additionally, the simulation was only capable of representing discrete lamp brightness levels of 0%, 50%, and 100%, in contrast to the experimental results, which showed a clear differentiation between 50%, 75%, and 100% brightness levels. Details of the model limitations are outlined with proposed solutions. The implications of the optimized system for auto insurance, liability coverage, and risk management are explored. These are areas that are rarely addressed in current research.

Statistical approach to the diagnosis of the dynamics of urban mobility under the influence of the road congestion situation in the city of Douala, Cameroon

2025-02-27T08:15:12+00:00

Smooth movement is an essential function and an indicator of a healthy city. Cities being engines of growth, congestion is a real cancer for the country’s economy. In this sense, linking travel habits and the increase in the level of congestion in a city is very important. The objective of this work is to diagnose, using a statistical approach, the dynamics of urban mobility influenced by the traffic congestion situation in the city of Douala. For this, the study focuses on two aspects; the first aspect concerns the multivariate descriptive analysis of motorists’ travel habits. The methods used involve first submitting surveys to motorists in vehicle technical inspection centers and processing the data in IBM SPSS statistical software. Follow-up of the analysis of the correlation between the congestion level and some required solutions. The second aspect focuses on the principal component analysis (PCA) that is performed. The determination of Kaiser-Meyer-Olkin (KMO) indices, the Bartlett significances, and the Pearson correlation coefficients are also done. The results show that the travel habits of motorists create a massive use of roads at certain specific time slots, in addition to extra-municipal trips mainly oriented towards the city center and the administrative district due to the monocentric situation of the city, which contributes to the increase in the level of congestion in the city. The correlation shows that there is significance between the level of congestion and the solutions considered, but this correlation is more or less moderate, which shows that the solutions considered can be used in the short term to alleviate congestion in the city.

Information System and Smart City

Energy-optimizing machine learning-driven smart traffic control system for urban mobility and the implications for insurance and risk management

Statistical approach to the diagnosis of the dynamics of urban mobility under the influence of the road congestion situation in the city of Douala, Cameroon