Coupling TS and ML Models for Short term Traffic Flow Prediction in ITS Supports

Description

Efficient traffic management is of utmost importance for efficient traffic flow, travel time reduction and environmentally efficient traffic systems. Reliable decision support tools are important for the development of Intelligent Transportation Systems (ITS) and sustainable urban traffic management. This study was therefore designed to investigate the most effective hybrid modelling strategy for traffic flow forecasting. Hourly traffic volume data across 20 road corridors in two directions were obtained from the Federal Highway Administration's (FHWA) Traffic Monitoring Analysis System (TMAS) for 2023. The traffic volume data were modelled using four model categories. The classical time-series (TS) models (STL-ETS; SARIMA, and Prophet); ML models (GBM, SVM, and RF) and hybrid models (TS_ML and ML_TS). TS_ML models were developed by applying the TS model to capture the linear and temporal structures in the traffic flow series, after which the resulting residuals were modelled using ML algorithms to account for the remaining non-linear patterns. Conversely, the ML-TS architecture initially employed ML models to learn the dominant non-linear relationships, while the residuals generated from the ML predictions were subsequently modelled using TS approaches to capture any remaining temporal dependencies and autocorrelation structures. Across all the road corridors, the most commonly selected architectures were Prophet_GBM (27.5%) and RF_Prophet (17.5%). Thirty-one hybrid-selected series achieved a mean NSE of 0.966 and mean KGE of 0.971. Forecasts from these models is important for the support of real-time ITS applications such as traffic congestion management, and incident response planning.

Institutions

• Morgan State University

  Maryland, Baltimore
• University of Port Harcourt

  Rivers State, Port Harcourt

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