Traffic Volume Prediction: Application Practice of Machine Learning and Deep Learning in Intelligent Transportation

This article analyzes the GitHub project traffic-volume-prediction (author: nhuynguyen06, published on 2026-05-28), exploring how to use machine learning and deep learning technologies to predict traffic volume, providing data support and decision-making basis for intelligent transportation systems. The project covers the application of traditional machine learning (ARIMA, regression, ensemble methods), deep learning (RNN/LSTM, CNN, GNN, and hybrid architectures), involves technical details such as data preprocessing and model training optimization, and elaborates on its value in scenarios like intelligent navigation and traffic signal control.

Traffic congestion is a common challenge in modern cities; urbanization and the growth of motor vehicles have exacerbated the contradiction of road resources. Traditional traffic management relies on manual experience and is difficult to cope with dynamic changes. Traffic volume prediction is a core technology of intelligent transportation, which can help management departments ease traffic congestion, drivers plan routes, and planners evaluate road demand. The traffic-volume-prediction project provides solutions based on machine learning and deep learning.

1. Spatiotemporal complexity: Road segments affect each other spatially; temporally, there are periodicity (daily/weekly/seasonal) and randomness (emergencies, weather); 2. Multi-factor correlation: Difficulty in integrating heterogeneous data such as time (time period/weekday/holiday), weather (rain/snow/visibility), events (activities/accidents/construction), and road network (grade/lane/intersection); 3. Real-time requirement: Models need to quickly process large amounts of data and output results to support real-time decision-making.

Before the rise of deep learning, traditional models were relied on: time series models (ARIMA, SARIMA to capture periodicity and trends), regression models (linear regression, SVR, random forest converted into regression problems), and ensemble methods (XGBoost, LightGBM to process tabular data). The effect depends on feature engineering; common features include lag features, statistical features, time features, spatial features, and external features.

• RNN and its variants (LSTM, GRU): Process time series; LSTM solves gradient vanishing through gating mechanisms;
• CNN: Convert road networks into grid/graph structures to capture spatial correlations;
• GNN: Directly model road network graph structures, which better conforms to topological characteristics;
• Hybrid architectures: CNN-LSTM (space + time), GNN-RNN (road network + dynamics), Transformer (attention mechanism for long-sequence prediction).

Data preprocessing: Missing value filling (interpolation/mean/model), outlier detection, data normalization, time alignment; Model training strategies: Split training/validation/test sets by time to avoid leakage, loss functions (MSE, MAE), evaluation metrics (MAE, RMSE, MAPE); Optimization techniques: Hyperparameter tuning, regularization (Dropout, L2), early stopping.

• Intelligent navigation: Dynamic route planning to avoid congestion;
• Traffic signal control: Dynamic adjustment of signal timing;
• Congestion warning: Early identification of congested road segments and times;
• Urban planning support: Long-term data to assist road planning and public transport layout.

The traffic-volume-prediction project demonstrates the potential of machine learning in intelligent transportation; technology has developed rapidly from traditional time series to deep learning. Recommendations for developers: Understand data characteristics, master multiple modeling methods, and focus on actual application needs. In the future, this technology will play a more important role in alleviating congestion and improving travel efficiency.