Mathematical Optimization of Intelligent Transportation Networks for Smart City Efficiency
Keywords:
congestion mitigation, governance capacity, intelligent transportation systems, smart cities, traffic optimizationAbstract
Purpose:This study investigates the role of optimizing intelligent transportation networks in enhancing the mobility outcomes of smart cities and the role of governance capacity in moderating these outcomes in the urban systems of the world. We present and validate the SmartTransMathOpt Model using a harmonized secondary data set of smart cities employing algorithm-based traffic management for the years 2019-2025.
Methods: We evaluate traffic flow algorithms, route scheduling, and real time data integration as core optimization processes, and assess to what extent these processes are related to time savings, reduction in congestion, energu efficiency, and reliability of the service, using standardized metrics and moderated regression analysis.
Findings:We establish that among the efficiency and reliability outcomes, real time data integration had the strongest and most consistent impact, while traffic flow algorithms and route scheduling had different, but complementary impacts. While governance capacity does not replace technological intensity, it determines whether the optimizations translate into rational and scalable performance improvements. Most importantly, this study identifies the governance conditioned optimization pathway, which explains why similar smart mobility technologies implemented in different cities produce divergent impacts.
Value: In smart city systems theory, this study refines the body of knowledge and for urban policy makers and planners globally, it provides actionable insights to effectively integrate data driven and resilient with sustainable transportation systems.
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