AI-Driven Geometric Highway Design Optimization for Enhancing Traffic Safety, Operational Efficiency, and Sustainable Mobility

The integration of artificial intelligence into highway geometric design represents a transformative paradigm in modern transportation engineering. Traditional road design procedures often rely on deterministic standards and iterative manual adjustments, which may inadequately capture the complex interactions among driver behavior, traffic flow characteristics, terrain constraints, environmental impacts, and safety performance. This study proposes an AI-driven framework for optimizing highway geometric design parameters, including horizontal curve radius, vertical alignment, sight distance, lane configuration, and superelevation, with the objective of improving safety, operational efficiency, and sustainability. Machine learning and metaheuristic optimization algorithms can be employed to analyze historical crash data, traffic volume patterns, topographic conditions, and design alternatives in order to identify geometries that minimize collision risk while maintaining acceptable levels of service. The proposed approach enables data-informed decision-making by predicting safety outcomes and operational performance prior to construction. Furthermore, AI-based optimization can support multi-objective trade-off analysis between construction cost, travel time, emissions, and design consistency. By bridging transportation design principles with advanced computational intelligence, this research contributes to the development of adaptive, evidence-based, and resilient roadway infrastructure. The findings may provide practical guidance for engineers, planners, and policymakers seeking to modernize highway design practices in the era of intelligent transportation systems.

کلیدواژه ها:

Artificial Intelligence ، Highway Geometric Design ، Traffic Safety Optimization ، Machine Learning ، Intelligent Transportation Systems