Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks

TLDR

Short‑term link travel‑time prediction is essential for route guidance systems, which require routes based on historical, real‑time, and anticipatory travel‑time information. The study examines using real‑time ITS data to predict link travel times for one to five consecutive 5‑minute periods. Historical link travel times are first clustered via unsupervised learning, then a modular ANN is trained for each cluster to predict future travel times, and the model is evaluated on Houston Transtar data. The modular ANN outperformed a conventional singular ANN and surpassed Kalman filtering, exponential smoothing, historical, and real‑time profile methods, achieving the best overall prediction accuracy.

Abstract

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.