Data transmission and AI-driven computation with image processing: A comprehensive review of traffic optimization frameworks

The integration of AI-driven computation, real-time data streams, and image processing is reshaping traffic management and urban logistics optimization. This research builds on the NarrQuest system—a globally pioneering narrative–computational methodology formalized through a five-article methodological canon and fifty published monographs—to introduce a first-of-its-kind logbook-based optimization framework. In this approach, personal journey narratives are encoded into structured search heuristics, transforming subjective records into formal routing constraints. Classical combinatorial optimization models, from Travelling Salesman Problems (TSP) to Vehicle Routing Problems (VRP), Integer Programming (IP), Scheduling, and Queueing Theory, are reformulated within this narrative optimization paradigm. Using multi-campus delivery datasets, exhaustive enumeration resolves small-scale TSP instances, while VRP formulations incorporate multi-agent constraints such as vehicle capacity and time windows. Integer Programming enhances modeling flexibility under contextual constraints, and Scheduling with Queueing theory stabilizes dynamic system performance. Large-scale complexity is addressed through AI-powered metaheuristics, particularly Genetic Algorithms for adaptive routing. Real-time image processing—including computer vision traffic sensing and behavior-informed demand forecasting—further strengthens responsive decision-making. Game-theoretic models capture strategic interaction within dynamic logistics ecosystems. This work positions NarrQuest not merely as a theoretical contribution, but as the first narrative-driven computational architecture with demonstrated capacity to meet other indexed algorithmic publication standards, bridging lived experience and intelligent logistics computation.