Evaluating the Efficacy of Deep Learning Architectures in Predicting Traffic Patterns for Smart City Development
Mohamed Ahmed Kandel1, Faris H. Rizk2, Lima Hongou3, Ahmed Mohamed Zaki4, Hakan Khan5, El-Sayed M. El-Kenawy*6
1 Department of Architecture, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
3 Faculty of Engineering, Computer Technology, UCSI University, Kuala Lumpur 56000, Malaysia
4 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
5 Department of Industrial Technology Engineering, Turkish-German University, Istanbul 34820, Turkey
6 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Emails: CH1800230@dhiet.edu.eg; faris.rizk@jcsis.org; hongou.Li12@gmail.com; azaki@jcsis.org; hakankhan24@mail.com; skenawy@ieee.org
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.
Keywords: Smart Cities; Traffic Pattern Prediction; Deep Learning Architectures; VGG16Net; VGG19Net; GoogLeNet; ResNet-50; AlexNet; Urban Development; Traffic Management.