Comparison of several short-term traffic speed forecasting models

2016/09/06 by John Boaz Lee, Lee, John Boaz, Kardi Teknomo +1
Computer Science · Engineering · #Applications (stat.AP) #Computation (stat.CO) #Computer Vision and Pattern Recognition (cs.CV) #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Mathematics #Probability (math.PR) #Time Series Analysis and Forecasting #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.1609.02409

openalex publication_date 2016/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The widespread adoption of smartphones in recent years has made it possible for us to collect large amounts of traffic data. Special software installed on the phones of drivers allow us to gather GPS trajectories of their vehicles on the road network. In this paper, we simulate the trajectories of multiple agents on a road network and use various models to forecast the short-term traffic speed of various links. Our results show that traditional techniques like multiple regression and artificial neural networks work well but simpler adaptive models that do not require prior training also perform comparatively well.

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