Day-to-Day Travel Time Trends and Travel Time Prediction from Loop Detector Data

This paper presents an approach to estimate future travel times on a freeway using flow and occupancy data from single loop detectors and historical travel time information. The work uses linear regression with stepwise variable selection method and more advanced tree based methods. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without under-estimation. The current traffic state proved to be a good predictor for the near future, up to 20 minutes, while historical data is more informative for longerrange predictions. Tree based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, this study uses real traffic data. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal of this research is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the paper examines how travel times change from day-to-day and develops several metrics to quantify these changes. The metrics can be used as input for travel time prediction, but they should be also beneficial for other applications such as calibrating traffic models and planning models.