This investigation delves into the critical challenges of urban development and management, employing a comprehensive evaluation of four strategic alternatives: transit-oriented development, green infrastructure investment, smart city technologies, and community-based development. These alternatives are rigorously assessed against a set of eight meticulously chosen criteria. Distinct from conventional analyses, the study adopts the sophisticated Criteria Importance Through Inter-criteria Correlation (CRITIC)-Weighted Aggregated Sum Product Assessment (WASPAS) methodology, utilizing spherical fuzzy sets (SFS). This approach mitigates uncertainties inherent in decision-making processes, thereby refining the accuracy of the evaluation. The CRITIC-WASPAS method, with its innovative application in this context, augments the precision of the assessments, yielding a detailed appraisal of each alternative's merits and limitations. Through assigning weighted criteria and systematically ranking these alternatives, the study furnishes pivotal insights for urban planners and policymakers. This contribution is instrumental in guiding decisions that promote resilience, equity, and environmental sustainability in urban environments. The novel integration of the CRITIC-WASPAS method in this domain not only propels the field forward but also lays a robust foundation for informed and effective decision-making. The outcomes of this research are poised to significantly impact the discourse on sustainable urban development, offering a data-driven framework that is essential for sculpting the future of cities amidst evolving urban challenges.

The burgeoning expansion of the Internet of Things (IoT) technology has propelled Intelligent Traffic Systems (ITS) to the forefront of IoT applications, with accurate highway traffic flow prediction models playing a pivotal role in their development. Such models are essential for mitigating highway traffic congestion, reducing accident rates, and informing city planning and traffic management strategies. Given the inherent periodicity, non-linearity, and variability of highway traffic data, an innovative model leveraging a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM) is proposed. In this model, feature extraction is accomplished via the CNN, which subsequently feeds into the BiLSTM for processing temporal dependencies. The integration of an AM enhances the model by weighting and fusing the BiLSTM outputs, thereby refining the prediction accuracy. Through a series of experiments and the application of diverse evaluation metrics, it is demonstrated that the proposed CNN-BiLSTM-AM model surpasses existing models in prediction accuracy and explainability. This advancement positions the model as a significant contribution to the field, offering a robust and insightful tool for highway traffic flow prediction.