Abstract
This article presents an integrated, multidisciplinary research framework that synthesizes advances in crowdsensing for pavement condition monitoring, mixed-traffic urban traffic control, intelligent transportation system security, platoon formation under mixed traffic, smart urban mobility data fusion, and metaheuristic optimization methods for signal timing and routing. We argue that urban mobility challenges—characterized by heterogeneous vehicle types, intermittent connectivity, infrastructure degradation, and competing objectives of safety, efficiency, and sustainability—require a tightly coupled approach that blends sensor-rich, crowdsourced data with adaptive control strategies and robust optimization. Building on empirical and theoretical results from recent literature, the proposed framework combines crowdsensing pipelines for continuous pavement health assessment (Jan et al., 2023), license-plate and vehicle-identification-driven traffic control (Li et al., 2023), receding-horizon platoon control that prioritizes safety in mixed flows (Mahbub et al., 2023), and deep-learning-based security measures for digital twin environments (Lv et al., 2021). We extend classical and contemporary metaheuristic approaches—particularly multi-objective particle swarm optimization and co-evolutionary strategies (Durillo et al., 2009; Goh et al., 2010; Mostaghim & Teich, 2003)—to derive coordinated signal timing, rerouting, and platoon coordination schemas that explicitly incorporate pavement state, privacy-preserving vehicle data, and resilience to adversarial threats. Detailed methodological exposition includes data fusion architectures, control law design, optimization problem formulation (described textually), evaluation metrics, and a rigorous discussion of limitations, counter-arguments, and future research directions. The contribution aims to guide both theoretical researchers and practitioners seeking to deploy next-generation smart-city mobility solutions that are safe, sustainable, and operational under real-world constraints. (Keywords: Crowdsensing, Mixed Traffic, Platoon Control, Intelligent Transportation Systems, Multi-objective Optimization)