Adaptive AI-Driven Dynamic Lighting Control System for Smart Streets and Highways for Balancing Safety and Energy Efficiency

• Rupa Sai. Nimmagadda,
• Preethi. Gowridevi,
• Lakshmi Sravani. Gorantla,
• Chintalapudi V Suresh,
• P. Umapathi Reddy,
• Kaki Ramya Sree,

Vol. 19, No. 9, September 30, 2025
10.3837/tiis.2025.09.009, Download Paper (Free):

• Dynamic lighting control
• dual-branch architecture
• Energy Efficiency
• Hybrid LSTM-Dense Neural Network
• Smart Cities

Abstract

Efficient and adaptive lighting systems are crucial for ensuring safety, energy conservation, and sustainability in smart cities. The rising demand for energy-efficient and adaptive street and highway lighting systems has prompted the investigation of intelligent control mechanisms. Conventional and semi-automated systems frequently exhibit elevated energy consumption and restricted adaptability to variable conditions, including traffic density, weather, and pedestrian activity. This research introduces an innovative Advanced Hybrid LSTM-Dense Neural Network (AHL-DNN), which employs a dual-branch architecture to overcome existing limitations. The model attains a classification accuracy of 99.72%, exceeding baseline models such as the Unified Dense Neural Network (UDNN) at 99.26% and conventional architectures like CNNs at 97.31%. Dynamically optimizing lighting levels enhances energy efficiency, resulting in estimated energy savings of up to 38-42% compared to static systems. Scalability is achieved via compatibility with edge AI and federated learning, facilitating real-time adaptability and maintaining data privacy. The AHL-DNN model exhibits high precision (99.72%), recall (100%), and F1-score (99.7%), establishing it as an effective solution for intelligent lighting control in smart cities.

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