Short-Term Traffic Speed Prediction and Pattern Discovery Using Sensor Data

Abstract

The sustained growth of urban populations has worsened chronic traffic congestion, with detrimental impacts on multiple dimensions of urban livability, including increased commute times, reduced road safety, and the degradation of local air quality. Thus, traffic speed prediction (TSP) is an important component of intelligent transportation systems (ITSs) because urban traffic behavior is dynamic, nonlinear, and time-dependent. This study addresses the challenge of short-term TSP using time-series regression techniques applied to the METR-LA dataset utilizing a multi-sensor subset containing one week of data. After reconstruction into a tabular format, the data were preprocessed using interpolation via forward/backward filling, and transformed using temporal and lag-based features. On the modeling side, a comparative analysis with Linear Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) models was conducted, while clustering provided additional insights into traffic-state patterns. Results indicated that KNN achieved the best performance after tuning (MAE = 2.22, RMSE = 4.38, R 2 = 0.899). The findings illustrated that simple interpretable models can support TSP and traffic-state understanding.