The escalating complexity of urban mobility systems, driven by rapid urbanization and increasing vehicular congestion, has underscored the need for accurate and adaptive traffic flow prediction models within the framework of smart cities. Traditional forecasting approaches often fail to capture the nonlinear, time-varying, and uncertain characteristics inherent in traffic data. To address these challenges, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) offer a compelling hybrid solution by integrating the human-like reasoning capabilities of fuzzy logic with the adaptive learning strengths of neural networks. This chapter presents a comprehensive study on the implementation of ANFIS for real-time traffic flow prediction, emphasizing data preprocessing, dynamic feature selection, parameter tuning, and model optimization. The chapter explores the architectural design of ANFIS, highlighting techniques for constructing efficient rule bases, optimizing membership functions, and configuring learning parameters such as step size, epoch limits, and error thresholds. Advanced training strategies utilizing evolutionary algorithms and metaheuristics are introduced to enhance convergence stability and generalization performance. The effectiveness of the model is validated using real-world traffic datasets, supported by detailed visualizations of prediction outcomes and error distribution. Furthermore, the study examines deployment challenges, scalability concerns, and integration with smart city infrastructure such as edge computing and IoT-enabled sensor networks. By addressing key limitations in traditional models and offering interpretable, data-driven insights, ANFIS is positioned as a powerful tool in the advancement of intelligent transportation systems. The findings underscore its potential to support real-time traffic management, congestion mitigation, and adaptive control mechanisms essential for the evolution of sustainable, resilient urban mobility ecosystems.
The unprecedented rate of urbanization has intensified the demand for intelligent transportation systems (ITS) capable of managing complex traffic scenarios in real time [1]. Metropolitan regions now face considerable challenges including increased vehicular congestion, prolonged travel times, elevated emissions, and frequent network disruptions [2]. Traditional urban planning strategies and static traffic control mechanisms have proven inadequate in mitigating these emerging problems [3]. In response, smart city initiatives have prioritized the integration of data- driven technologies for adaptive traffic management. Among these, predictive modeling of traffic flow stands as a cornerstone, offering the potential to forecast congestion, optimize traffic signal timings, and support dynamic route guidance [4]. The nonlinear and time-varying nature of urban traffic poses significant difficulties for conventional forecasting techniques, which often assume stationarity, linearity, or fixed temporal dependencies in the underlying data [5].
In addressing these complexities, soft computing paradigms have gained traction, particularly those capable of handling imprecise, noisy, and dynamic input data [6]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) have emerged as a hybrid approach that combines the interpretability of fuzzy logic systems with the learning capability of artificial neural networks [7]. This dual architecture allows ANFIS to model uncertain, nonlinear relationships within high-dimensional traffic datasets, while also adapting in real time to fluctuating patterns [8]. Unlike purely statistical or black-box AI models, ANFIS maintains a rule-based structure, offering transparency and interpretabilityâ€â€Âtwo critical features in public infrastructure management [9]. The capability to learn from streaming data while preserving logical inference makes ANFIS particularly suitable for deployment in smart transportation ecosystems where both accuracy and explainability are essential [10].ÂÂÂ
