Evaluating the Efficacy of Deep Learning Architectures in Predicting Traffic Patterns for Smart City Development

Abstract

Smart city development necessitates the implementation of effective traffic management strategies. In this vein, various deep learning architectures, including VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet, are employed to predict diverse traffic patterns extracted from a comprehensive dataset. Evaluating performance metrics such as accuracy, sensitivity, and specificity reveals discernible variations among models, with ResNet-50 and AlexNet demonstrating superior predictive capabilities. Descriptive statistics and statistical analyses, including ANOVA and the Wilcoxon Signed Rank Test, provide nuanced insights into model differences and significance. The findings bear significant implications for urban planners and policymakers transforming cities into intelligent ecosystems, offering valuable insights for informed decision-making in innovative city development. Improved traffic predictions enhance daily commuting experiences and contribute to the informed development of sustainable urban infrastructure, aligning seamlessly with the ongoing evolution of smart cities toward a more connected and efficient future. Notably, AlexNet exhibits a significant accuracy of 0.931780366 in the context of traffic pattern prediction.