Deep Learning-Based Intelligent Traffic Prediction System for Smart Cities

Abstract

Rapid urbanization has intensified congestion, travel-time uncertainty, fuel consumption, and emissions, making reliable short-term traffic prediction a central requirement of smart-city intelligent transportation systems. This methodology paper proposes a deep learning-based intelligent traffic prediction system that combines multivariate traffic sensing, temporal sequence modeling, and spatial road-network information. The framework integrates traffic flow, speed, occupancy, time-of-day, weather, and event-related variables; applies missing-value treatment, normalization, temporal windowing, and feature fusion; and then learns nonlinear spatio-temporal patterns through a hybrid CNN-LSTM architecture with an optional graph-convolution layer for network-aware modeling. The proposed evaluation protocol uses benchmark-style train/validation/test splits and standard forecasting measures including MAE, RMSE, and MAPE. The design is grounded in published evidence up to 2021 showing that deep architectures, LSTM networks, convolutional models, and graph-based networks improve the representation of complex traffic dynamics compared with shallow or purely statistical methods. As no primary dataset or experimental output was supplied for this paper, the results section reports a literature-grounded methodological comparison rather than fabricated new measurements. The proposed system is suitable for applications such as adaptive signal control, route guidance, congestion alerts, and proactive traffic management in smart cities.