Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems

2025-10-26

AI Summary

To address the challenge of effectively modeling multi-scale, nonlinear, and highly noisy temporal patterns in traffic flow data—features poorly captured by single-model approaches—this paper proposes a hybrid forecasting framework based on Seasonal and Trend decomposition using Loess (STL). The original time series is decomposed into trend, seasonal, and residual components, each modeled by a specialized algorithm: LSTM for long-term dependencies, ARIMA for periodic patterns, and XGBoost for nonlinear residuals. Final predictions are obtained via multiplicative ensemble integration. Compared to conventional single-model baselines, the proposed framework achieves superior prediction accuracy, enhanced interpretability, and improved robustness. Experiments on New York City traffic flow data demonstrate reductions in MAE and RMSE exceeding 15%, alongside an R² improvement of approximately 0.08, validating the efficacy of multi-component decomposition coupled with heterogeneous model collaboration.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate traffic flow forecasting is essential for intelligent transportation systems and urban traffic management. However, single model approaches often fail to capture the complex, nonlinear, and multi scale temporal patterns in traffic flow data. This study proposes a decomposition driven hybrid framework that integrates Seasonal Trend decomposition using Loess (STL) with three complementary predictive models. STL first decomposes the original time series into trend, seasonal, and residual components. Then, a Long Short Term Memory (LSTM) network models long term trends, an Autoregressive Integrated Moving Average (ARIMA) model captures seasonal periodicity, and an Extreme Gradient Boosting (XGBoost) algorithm predicts nonlinear residual fluctuations. The final forecast is obtained through multiplicative integration of the sub model predictions. Using 998 traffic flow records from a New York City intersection between November and December 2015, results show that the LSTM ARIMA XGBoost hybrid model significantly outperforms standalone models including LSTM, ARIMA, and XGBoost across MAE, RMSE, and R squared metrics. The decomposition strategy effectively isolates temporal characteristics, allowing each model to specialize, thereby improving prediction accuracy, interpretability, and robustness.

Problem

• Forecasting complex nonlinear traffic flow patterns
• Integrating STL decomposition with hybrid predictive models
• Improving prediction accuracy through temporal component specialization

Innovation

• STL decomposition separates traffic data components
• LSTM, ARIMA, XGBoost model different temporal patterns
• Multiplicative integration combines specialized sub-model predictions