Travel Time Prediction on Highways

Abstract

We describe the development of a predictive model for vehicle journey time on highways. Accurate travel time prediction is an important problem since it enables planning of cost effective vehicle routes and departure times, with the aim of saving time and fuel while reducing pollution. The main information source used is data from roadside double inductive loop sensors which measure vehicle speed, flow and density at specific locations. We model the spatiotemporal distribution of travel times by using local linear regression. The use of real-time data is very accurate for shorter journeys starting now and less reliable as journey times increase. Local linear regression can be used to optimally balance the use of historical and real time data. The main contribution of the paper is the extension of local linear models with higher order autoregressive travel time variables, namely vehicle flow data, and density data. Using two years of UK Highways Agency (HA) loop sensor data we found that the extended model significantly improves predictive performance while retaining the main benefits of earlier work: interpretability of linear models as well as computationally simple predictions.