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

Abstract

An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. 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 underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range 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, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.

How to Provide Accurate and Robust Traffic Forecasts Practically?

“…For instance, most of literature foucs on traffc flow forecasting (Jiang & Adeli, 2004;Qiao et al, 2001;Abdulhai et al, 1999;Castillo et al, 2008;Chen & Chen, 2007;Dimitriou et al, 2008;Ding et al, 2002;Huang & Sadek, 2009;Ghosh et al, 2005Ghosh et al, , 2007Smith et al, 2002), travel time forecasting, and related analysis such as validation, optimization, etc. (Chan et al, 2003;Chang et al, 2010;Kwon, 2000;Kwon & Petty, 2005;Lam, 2008;Lam et al, 2002Lam et al, , 2008Lam & Chan, 2004;Lee et al 2009;Nath et al, 2010;Schadschneider et al, 2005;Tam & Lam, 2009;Tang & Lam, 2001;Yang et al, 2010).…”

Effective long-term travel time prediction with fuzzy rules for tollway

“…It's noted short term travel time prediction for freeways and motorways has been intensively studied over the last two decades [2][3][4][5][6][7][8]. In contrast, the researches on long term prediction lagged well behind for many reasons, even though theoretical foundations for long term predictions on generic time series had been laid many decades ago.…”

Soft Computing-Based Traffic Density Estimation Using Automated Traffic Sensor Data under Indian Conditions

“…Other methods such as linear regression and Kalman filtering are widely used in the area of prediction. Kwon et al 10 estimated travel time on a freeway, using flow and occupancy data obtained from loop detectors and predicted to future time steps using linear regression. Kalman filter method was employed by Chen and Chein 11 to predict travel time using data obtained from probe vehicles.…”