Using link travel time covariance information to predict dynamic journey times in stochastic road networks

Abstract

Journey time prediction is a crucial component in advanced traveler information systems for helping travelers in making their travel decisions. This paper investigates the journey time prediction problem in road network with stochastic journey times and link flows. The proposed prediction framework consists of two sub-modules. The first one is a reliability-based dynamic traffic assignment model to establish a database for the historical traffic conditions, while the other sub-module, which is a multi-level k-NN model for predicting journey times based on the historical records in the database. A Sioux Falls road network example is used to demonstrate the accuracy, efficiency and robustness of the proposed framework for the journey time prediction problem in stochastic network with uncertainties.